Probability-based usage of multiple estimators of a physiological signal

ABSTRACT

A physiological signal such as a heart rate acquired from a monitoring device is processed to reduce interference, ambiguity, or artifacts arising during various activities. For example, the system can process a physiological signal to account for motion artifacts in the physiological signal and, thus, reduce the impact of movement on the physiological signal. Additionally, or alternatively, the system can process a physiological signal based on one or more measurement contexts associated with a wearable device. In general, the physiological signal processed as described herein can be useful as a reliable, continuous indication of a physiological parameter and, thus, can serve as the basis for other physiological assessments (e.g., heart rate variability) derived from the physiological parameter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 62/218,017, filed on Sep. 14, 2015, the entirecontent of which is hereby incorporated by reference.

This application is also related to the following commonly-owned patentapplications each incorporated herein by reference in its entirety: U.S.patent application Ser. No. 15/265,792, filed on Sep. 14, 2016, andentitled “Removing Motion Artifacts from a Physiological Signal,” andInternational Patent Application Serial No. PCT/US16/51757, filed onSep. 14, 2016, and entitled “Physiological Signal Monitoring.”

BACKGROUND

Physiological monitoring of an individual during periods of activity andrest is useful for assessing the monitored individual's health andfitness. Such physiological monitoring is often carried out usingwearable devices, which can be particularly convenient, as wearabledevices generally do not interfere with the monitored individual'smobility. Thus, wearable devices can be worn during a wide range ofactivities. Certain activities and environments, however, can interferewith physiological monitoring.

SUMMARY

A physiological signal such as a heart rate acquired from a monitoringdevice is processed to reduce interference, ambiguity, or artifactsarising during various activities. For example, the system can process aphysiological signal to account for motion artifacts in thephysiological signal and, thus, reduce the impact of movement on thephysiological signal. Additionally, or alternatively, the system canprocess a physiological signal based on one or more measurement contextsassociated with a wearable device. In general, the physiological signalprocessed as described herein can be useful as a reliable, continuousindication of a physiological parameter and, thus, can serve as thebasis for other physiological assessments (e.g., heart rate variability)derived from the physiological parameter.

In an aspect, a method for concurrent use of multiple heart rateestimation techniques may include providing a plurality of heart rateestimators for estimating a heart rate, where each one of the pluralityof heart rate estimators optimized for one of a number of predeterminedmeasurement contexts for measuring the heart rate with a wearablephysiological monitor, and providing a plurality of probabilityestimators, each one of the plurality of probability estimatorscorresponding to one of the plurality of heart rate estimators, and eachone of the plurality of probability estimators providing a likelihoodthat a corresponding heart rate estimator is accurately estimating theheart rate based on a physiological signal from the wearablephysiological monitor. The method may also include acquiring aphysiological signal from the wearable physiological monitor over aninterval having segments, for each segment of the interval, assigningone or more selected ones of the plurality of heart rate estimators tothe segment according to the likelihood of accurately estimating theheart rate in the segment, and providing a heart rate signal over theinterval based upon the physiological signal and the selected ones ofthe plurality of heart rate estimators.

Implementations may include one or more of the following features.Assigning one or more selected ones of the plurality of heart rateestimators may include fusing multiple heart rate estimators to create afused estimator and applying the fused estimator to determine a heartrate for the segment. The method may further include determining a heartrate variability over the interval based upon the heart rate signal. Theheart rate signal may be a heart rate variability signal. The number ofpredetermined measurement contexts may include one or more of an indooractivity, an outdoor activity, one or more physiological parameters, andan ambient weather condition. The number of predetermined measurementcontexts may include one or more of active, sedentary, and sleeping. Thenumber of predetermined measurement contexts may include one or moretypes of physical exercise. The number of predetermined measurementcontexts may include one or more types of motion of the wearablephysiological monitor based on motion data from one or more motionsensors in the wearable physiological monitor. The number ofpredetermined measurement contexts may include a current heart rateestimated for the wearable physiological monitor. The heart rateestimators may include a frequency domain peak detector for detecting atleast one of a fundamental peak and one or more harmonic peaks. Theheart rate estimators may include a fundamental frequency of a harmonicproduct spectrum for the physiological signal. The method may furtherinclude creating a preliminary estimate of a most probable heart ratebased on one or more factors, and after creating the preliminaryestimate, applying the plurality of heart rate estimators based on theprobability estimators to increase a probability of an accurateestimate. The preliminary estimate may be based on one or more of aprior heart rate, a subsequent heart rate, and a history for a user ofthe wearable physiological monitor. The preliminary estimate may befiltered to remove one or more motion artifacts before applying theplurality of probability estimators. The heart rate estimators mayinclude at least one estimator with a high-Q time domain filter for thephysiological signal around a predetermined frequency of interest. Theheart rate estimators may include an estimator that tracks peaks in thephysiological signal relative to a motion signal.

In an aspect, a computer program product may include non-transitorycomputer executable code embodied in a non-transitory computer-readablemedium that, when executing on one or more computing devices, performsthe steps of providing a plurality of heart rate estimators forestimating a heart rate, where each one of the plurality of heart rateestimators corresponding to one of a number of predetermined measurementcontexts for measuring the heart rate with a wearable physiologicalmonitor, and providing a plurality of probability estimators, each oneof the plurality of probability estimators corresponding to one of theplurality of heart rate estimators, and each one of the plurality ofprobability estimators providing a likelihood that a corresponding heartrate estimator is accurately estimating the heart rate based on aphysiological signal from the wearable physiological monitor. Thecomputer program product may also include code that performs the stepsof acquiring a physiological signal from the wearable physiologicalmonitor over an interval having segments, for each segment, assigningone or more selected ones of the plurality of heart rate estimators tothe segment based upon the likelihood of accurately estimating the heartrate over in the segment, and providing a heart rate signal over theinterval based upon the physiological signal and the selected ones ofthe plurality of heart rate estimators.

Implementations may include one or more of the following features. Thesegments may be discrete segments of the interval. The heart rate signalmay be continuously provided over the interval. The number ofpredetermined measurement contexts may include one or more types ofphysical activity. The plurality of heart rate estimators may include atleast one or more of a frequency domain peak detector, a fundamentalfrequency of a harmonic product spectrum for the physiological signal, afundamental frequency of a complex cepstrum for the physiologicalsignal, a high-Q time domain filter for the physiological signal arounda predetermined frequency of interest, and an estimator that trackspeaks in the physiological signal relative to a motion signal.

In an aspect, a system may include a memory configured to store datacorresponding to a physiological signal over an interval, where thephysiological signal is acquired by a wearable physiological monitor,and a server configured to assign, for each segment of the interval, oneor more selected heart rate estimators of a plurality of heart rateestimators based on a likelihood of accurately estimating the heart ratein the segment and to provide a continuous heart rate signal over theinterval based upon the physiological signal and the one or moreselected heart rate estimators, each heart rate estimator of theplurality of heart rate estimators optimized for one of a number ofpredetermined measurement contexts for measuring the heart rate with thewearable physiological monitor.

Implementations may include one or more of the following features. Thelikelihood of accurately estimating the heart rate in the segment may bebased on a plurality of probability estimators, each one of theplurality of probability estimators corresponding to one of theplurality of heart rate estimators, and each one of the plurality ofprobability estimators providing a likelihood that a corresponding heartrate estimator is accurately estimating the heart rate based on thephysiological signal from the wearable physiological monitor. Theplurality of heart rate estimators may include at least one or more of afrequency domain peak detector, a fundamental frequency of a harmonicproduct spectrum for the physiological signal, a fundamental frequencyof a complex cepstrum for the physiological signal, a high-Q time domainfilter for the physiological signal around a predetermined frequency ofinterest, and an estimator that tracks peaks in the physiological signalrelative to a motion signal.

In an aspect, a method for mitigating motion artifacts in aphysiological signal from a wearable physiological monitoring device mayinclude acquiring a physiological signal with a device over time, wherethe device is a wearable device for physiological monitoring, andfurther where the physiological signal is indicative of a physical stateof a wearer of the device. The method may also include measuring motionof the device as a motion signal over time, determining a fundamentalfrequency of a spectrum of the motion signal, and applying a group ofnotch filters to the physiological signal, where the group of notchfilters attenuate the fundamental frequency of the motion signal and oneor more related frequencies of the motion signal to provide a filteredphysiological signal.

Implementations may include one or more of the following features. Theone or more related frequencies may include one or more harmonics of thefundamental frequency. The one or more related frequencies may include anumber of frequencies selected based on a model using multiplicativemodulation of the physiological signal and the motion signal. Thephysiological signal may be a signal indicative of a heart rate of thewearer. Acquiring the physiological signal may include receiving aphotoplesmythography signal from the device. Measuring the motion of thedevice may include detecting the motion with one or more accelerometers.Measuring the motion of the device may include detecting the motion in apredetermined axis of the device. The predetermined axis may be normalto a measurement surface of the wearer in contact with the device.Determining the fundamental frequency may include identifying a dominantpeak in the spectrum of the motion signal. Determining the fundamentalfrequency may include identifying the fundamental frequency using atleast one of a harmonic product spectrum and a complex cepstrum of thespectrum of the motion signal. The method may further includepreprocessing the spectrum of the motion signal to remove one or moreartifacts arising from time domain discontinuities in the motion signal.Applying the group of notch filters may include post-processing thephysiological signal on a remote computing resource that receives thephysiological signal and the motion signal from the device. The methodmay further include creating a model of a relationship between themotion and the physiological signal and applying the model to attenuatean artifact of the motion signal in the physiological signal.

In an aspect, a computer program product may include non-transitorycomputer executable code embodied in a non-transitory computer-readablemedium that, when executing on one or more computing devices, performsthe steps of acquiring a physiological signal over time from a devicefor physiological monitoring, where the physiological signal isindicative of a physical state of a wearer of the device, measuringmotion of the device as a motion signal over time, determining afundamental frequency of a spectrum of the motion signal, and applying anotch filter to the physiological signal, where the notch filterattenuates the fundamental frequency of the motion signal and one ormore related frequencies of the motion signal to provide a filteredphysiological signal.

Implementations may include one or more of the following features. Theone or more related frequencies may include one or more harmonics of thefundamental frequency. The one or more related frequencies may include anumber of frequencies selected based on a physical model of the deviceand the wearer, the physical model including multiplicative modulationof the physiological signal and the motion signal. The physiologicalsignal may be a signal indicative of a heart rate of the wearer.Determining the fundamental frequency may include one or more ofidentifying a dominant peak in the spectrum of the motion signal andidentifying the fundamental frequency using at least one of a harmonicproduct spectrum and a complex cepstrum of the spectrum of the motionsignal.

In an aspect, a system may include a memory configured to store aphysiological signal over time, the physiological signal indicative of aphysical state of a wearer of a device for physiological monitoring, andthe memory further configured to store a motion signal of the deviceover time. The system may also include a server configured to filter thephysiological signal based on applying a notch filter to thephysiological signal, where the notch filter attenuates a fundamentalfrequency of the motion signal and one or more related frequencies ofthe motion signal.

Implementations may include one or more of the following features. Theone or more related frequencies may include at least one of thefollowing: one or more harmonics of the fundamental frequency and anumber of frequencies selected based on a physical model of the deviceand the wearer, the physical model including multiplicative modulationof the physiological signal and the motion signal.

In an aspect, a method for mitigating motion artifacts in aphysiological signal from a wearable physiological monitoring device mayinclude acquiring a physiological signal with a device over time, wherethe device is a wearable device for physiological monitoring including aphysiological sensor to capture the physiological signal, and furtherwhere the physiological signal is indicative of a physical state of awearer of the device. The method may also include measuring motion ofthe device as a motion signal over time, where the motion signalincludes three-dimensional motion data from a three-axis motion sensingsystem of the device, determining a fundamental frequency of a spectrumof the motion signal in at least one axis, providing a modelestablishing a relationship between three-dimensional motion of thedevice and the physiological signal obtained from the physiologicalsensor, and applying the model to remove a motion artifact in thephysiological signal caused by the three-dimensional motion of thedevice based on the three-dimensional motion data from the three-axismotion sensing system.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedevices, systems, and methods described herein will be apparent from thefollowing description of particular embodiments thereof, as illustratedin the accompanying figures. The figures are not necessarily to scale,emphasis instead being placed upon illustrating the principles of thedevices, systems, and methods described herein.

FIG. 1 illustrates front and back perspective views of a wearable systemconfigured as a bracelet including one or more straps.

FIG. 2 shows a block diagram illustrating components of a wearablephysiological measurement system configured to provide continuouscollection and monitoring of physiological data.

FIG. 3 is a flowchart illustrating a signal processing algorithm forgenerating a sequence of heart rates for every detected heartbeat thatmay be embodied in computer-executable instructions stored on one ormore non-transitory computer-readable media.

FIG. 4 is a flowchart illustrating a method of determining an intensityscore.

FIG. 5 is a flowchart illustrating a method by which a user may useintensity and recovery scores.

FIG. 6 is a flow chart illustrating a method for detecting heart ratevariability in sleep states.

FIG. 7 is a bottom view of a wearable, continuous physiologicalmonitoring device.

FIG. 8 is a flow chart illustrating a method for concurrent use ofmultiple physiological parameter estimation techniques.

FIG. 9 is a flow chart illustrating a signal processing algorithm forremoving motion artifacts from a physiological signal.

DETAILED DESCRIPTION

The embodiments will now be described more fully hereinafter withreference to the accompanying figures, in which preferred embodimentsare shown. The foregoing may, however, be embodied in many differentforms and should not be construed as limited to the illustratedembodiments set forth herein. Rather, these illustrated embodiments areprovided so that this disclosure will convey the scope to those skilledin the art.

All documents mentioned herein are hereby incorporated by reference intheir entirety. References to items in the singular should be understoodto include items in the plural, and vice versa, unless explicitly statedotherwise or clear from the text. Grammatical conjunctions are intendedto express any and all disjunctive and conjunctive combinations ofconjoined clauses, sentences, words, and the like, unless otherwisestated or clear from the context. Thus, the term “or” should generallybe understood to mean “and/or” and so forth.

Recitations of ranges of values herein are not intended to be limiting,referring instead individually to any and all values falling within therange, unless otherwise indicated herein, and each separate value withinsuch a range is incorporated into the specification as if it wereindividually recited herein. The words “about,” “approximately,” or thelike, when accompanying a numerical value, are to be construed asincluding any deviation as would be appreciated by one of ordinary skillin the art to operate satisfactorily for an intended purpose, or whereapplicable, any acceptable range of deviation appropriate to ameasurement of the numerical value or achievable by instrumentation usedto measure the amount. Ranges of values and/or numeric values areprovided herein as examples only, and do not constitute a limitation onthe scope of the described embodiments. The use of any and all examples,or exemplary language (“e.g.,” “such as,” or the like) provided herein,is intended merely to better illuminate the embodiments and does notpose a limitation on the scope of the embodiments. No language in thespecification should be construed as indicating any unclaimed element asessential to the practice of the embodiments.

In the following description, it is understood that terms such as“first,” “second,” “above,” “below,” and the like, are words ofconvenience and are not to be construed as limiting terms.

Exemplary embodiments provide physiological measurement systems, devicesand methods for continuous health and fitness monitoring, and provideimprovements to overcome the drawbacks of conventional heart ratemonitors. One aspect of the present disclosure is directed to providinga lightweight wearable system with a strap that collects variousphysiological data or signals from a wearer. The strap may be used toposition the system on an appendage or extremity of a user, for example,wrist, ankle, and the like. Exemplary systems are wearable and enablereal-time and continuous monitoring of heart rate without the need for achest strap or other bulky equipment which could otherwise causediscomfort and prevent continuous wearing and use. The system maydetermine the user's heart rate without the use of electrocardiographyand without the need for a chest strap. Exemplary systems can thereby beused in not only assessing general well-being but also in continuousmonitoring of fitness. Exemplary systems also enable monitoring of oneor more physiological parameters in addition to heart rate including,but not limited to, body temperature, heart rate variability, motion,sleep, stress, fitness level, recovery level, effect of a workoutroutine on health and fitness, caloric expenditure, and the like.

A health or fitness monitor that includes bulky components may hindercontinuous wear. Existing fitness monitors often include thefunctionality of a watch, thereby making the health or fitness monitorquite bulky and inconvenient for continuous wear. Accordingly, oneaspect is directed to providing a wearable health or fitness system thatdoes not include bulky components, thereby making the bracelet slimmer,unobtrusive and appropriate for continuous wear. The ability tocontinuously wear the bracelet further allows continuous collection ofphysiological data, as well as continuous and more reliable health orfitness monitoring. For example, embodiments of the bracelet disclosedherein allow users to monitor data at all times, not just during afitness session. In some embodiments, the wearable system may or may notinclude a display screen for displaying heart rate and otherinformation. In other embodiments, the wearable system may include oneor more light emitting diodes (LEDs) to provide feedback to a user anddisplay heart rate selectively. In some embodiments, the wearable systemmay include a removable or releasable modular head that may provideadditional features and may display additional information. Such amodular head can be releasably installed on the wearable system whenadditional information display is desired, and removed to improve thecomfort and appearance of the wearable system. In other embodiments, thehead may be integrally formed in the wearable system.

Exemplary embodiments also include computer-executable instructionsthat, when executed, enable automatic interpretation of one or morephysiological parameters to assess the cardiovascular intensityexperienced by a user (embodied in an intensity score or indicator) andthe user's recovery after physical exertion or daily stress given sleepand other forms of rest (embodied in a recovery score). These indicatorsor scores may be stored and displayed in a meaningful format to assist auser in managing his health and exercise regimen. Exemplarycomputer-executable instructions may be provided in a cloudimplementation. Exemplary embodiments also include a website that, e.g.,allows users to monitor their own fitness results, share informationwith their teammates and coaches, compete with other users, and soforth. Both the wearable system and the website allow a user to providefeedback regarding his/her day, exercise and/or sleep, which enablesrecovery and performance ratings.

In an exemplary technique of data transmission, data collected by awearable system may be transmitted directly to a cloud-based datastorage, from which data may be downloaded for display and analysis on awebsite. In another exemplary technique of data transmission, datacollected by a wearable system may be transmitted via a mobilecommunication device application to a cloud-based data storage, fromwhich data may be downloaded for display and analysis on a website.

The term “user” as used herein, refers to any type of animal, human ornon-human, whose physiological information may be monitored using anexemplary wearable physiological monitoring system. The term “body,” asused herein, refers to the body of a user.

The term “continuous,” as used herein in connection with heart rate datacollection, refers to collection of heart rate data at a sufficientfrequency to enable detection of every heart beat and also refers tocollection of heart rate data continuously throughout the day and night.

The term “computer-readable medium,” as used herein, refers to anon-transitory storage hardware, non-transitory storage device ornon-transitory computer system memory that may be accessed by acontroller, a microcontroller, a computational system or a module of acomputational system to encode thereon computer-executable instructionsor software programs. The “computer-readable medium” may be accessed bya computational system or a module of a computational system to retrieveand/or execute the computer-executable instructions or software programsencoded on the medium. The non-transitory computer-readable media mayinclude, but are not limited to, one or more types of hardware memory,non-transitory tangible media (for example, one or more magnetic storagedisks, one or more optical disks, one or more USB flash drives),computer system memory or random access memory (such as, DRAM, SRAM, EDORAM) and the like.

Exemplary embodiments provide wearable physiological measurementssystems that are configured to provide continuous measurement of heartrate. Exemplary systems are configured to be continuously wearable on anappendage, for example, wrist or ankle, and do not rely onelectrocardiography or chest straps in detection of heart rate. Theexemplary system includes one or more light emitters for emitting lightat one or more desired frequencies toward the user's skin, and one ormore light detectors for received light reflected from the user's skin.The light detectors may include a photo-resistor, a photo-transistor, aphoto-diode, and the like. As light from the light emitters (forexample, green light) pierces through the skin of the user, the blood'snatural absorbance or transmittance for the light provides fluctuationsin the photo-resistor readouts. These waves have the same frequency asthe user's pulse since increased absorbance or transmittance occurs onlywhen the blood flow has increased after a heartbeat. The system includesa processing module implemented in software, hardware or a combinationthereof for processing the optical data received at the light detectorsand continuously determining the heart rate based on the optical data.The optical data may be combined with data from one or more motionsensors, e.g., accelerometers and/or gyroscopes, to minimize oreliminate noise in the heart rate signal caused by motion or otherartifacts (or with other optical data of another wavelength).

FIG. 1 illustrates front and back perspective views of one embodiment ofa wearable system configured as a bracelet 100 including one or morestraps 102. The bracelet is sleek and lightweight, thereby making itappropriate for continuous wear. The bracelet may or may not include adisplay screen, e.g., a screen 106 such as a light emitting diode (LED)display for displaying any desired data (e.g., instantaneous heartrate).

As shown in FIG. 1, the wearable system may include componentsconfigured to provide various functions such as data collection andstreaming functions of the bracelet. In some embodiments, the wearablesystem may include a button underneath the wearable system. In someembodiments, the button may be configured such that, when the wearablesystem is properly tightened to one's wrist, the button may press downand activate the bracelet to begin storing information. In otherembodiments, the button may be disposed and configured such that it maybe pressed manually at the discretion of a user to begin storinginformation or otherwise to mark the start or end of an activity periodsuch as sleep. In some embodiments, the button may be held to initiate atime stamp and held again to end a time stamp, which may be transmitted,directly or through a mobile communication device application, to awebsite as a time stamp.

The wearable system may include a heart rate monitor. In one example,the heart rate may be detected from the radial artery. Thus, thewearable system may include a pulse sensor. In one embodiment, thewearable system may be configured such that, when a user wears it aroundtheir wrist and tightens it, the sensor portion of the wearable systemis secured over the user's radial artery or other blood vessel. Secureconnection and placement of the pulse sensor over the radial artery orother blood vessel may allow measurement of heart rate and pulse. Itwill be understood that this configuration is provided by way of exampleonly, and that other sensors, sensor positions, and monitoringtechniques may also or instead be employed without departing from thescope of this disclosure.

In some embodiments, the pulse or heart rate may be taken using anoptical sensor coupled with one or more light emitting diodes (LEDs),all directly in contact with the user's wrist. The LEDs are provided ina suitable position from which light can be emitted into the user'sskin. In one example, the LEDs mounted on a side or top surface of acircuit board in the system to prevent heat buildup on the LEDs and toprevent burns on the skin. The circuit board may be designed with theintent of dissipating heat, e.g., by including thick conductive layers,exposed copper, heatsink, or similar. In one aspect, the pulserepetition frequency is such that the amount of power thermallydissipated by the LED is negligible. Cleverly designed elastic wriststraps can ensure that the sensors are always in contact with the skinand that there is a minimal amount of outside light seeping into thesensors. In addition to the elastic wrist strap, the design of the strapmay allow for continuous micro adjustments (no preset sizes) in order toachieve an optimal fit, and a floating sensor module. The sensor modulemay be free to move with the natural movements caused by flexion andextension of the wrist.

In some embodiments, the wearable system may be configured to recordother physiological parameters including, but not limited to, skintemperature (using a thermometer), galvanic skin response (using agalvanic skin response sensor), motion (using one or more multi-axesaccelerometers and/or gyroscope), and the like, and environmental orcontextual parameters, e.g., ambient temperature, humidity, time of day,and the like. In an implementation, sensors are used to provide at leastone of continuous motion detection, environmental temperature sensing,electrodermal activity (EDA) sensing, galvanic skin response (GSR)sensing, and the like. In this manner, an implementation can identifythe cause of a detected physiological event. ReflectancePhotoPlethysmoGraphy (RPPG) may be used for the detection of cardiacactivity, which may provide for non-intrusive data collection, usabilityin wet, dusty and otherwise harsh environments, and low powerrequirements. For example, as explained herein, using the physiologicalreadouts of the device and the analytics described herein, an “IntensityScore” (e.g., 0-21) (e.g., that measures a user's recent exertion), a“Recovery Score” (e.g., 0-100%), and “Sleep Score” (e.g., 0-100) maytogether measure readiness for physical and psychological exertion.

In some embodiments, the wearable system may further be configured suchthat a button underneath the system may be pressed against the user'swrist, thus triggering the system to begin one or more of collectingdata, calculating metrics and communicating the information to anetwork. In some embodiments, the sensor used for, e.g., measuring heartrate or GSR or any combination of these, may be used to indicate whetherthe user is wearing the wearable system or not. In some embodiments,power to the one or more LEDs may be cut off as soon as this situationis detected, and reset once the user has put the wearable system back ontheir wrist.

The wearable system may include one, two or more sources of batterylife, e.g., two or more batteries. In some embodiments, it may have abattery that can slip in and out of the head of the wearable system andcan be recharged using an included accessory. Additionally, the wearablesystem may have a built-in battery that is less powerful. When the morepowerful battery is being charged, the user does not need to remove thewearable system and can still record data (during sleep, for example).

In exemplary embodiments, the wearable system is enabled toautomatically detect when the user is asleep, awake but at rest andexercising based on physiological data collected by the system.

FIG. 2 shows a block diagram illustrating exemplary components of awearable physiological measurement system 200 configured to providecontinuous collection and monitoring of physiological data. The wearablesystem 200 includes one or more sensors 202. As discussed above, thesensors 202 may include a heart rate monitor. In some embodiments, thewearable system 200 may further include one or more of sensors fordetecting calorie burn, distance and activity. Calorie burn may be basedon a user's heart rate, and a calorie burn measurement may be improvedif a user chooses to provide his or her weight and/or other physicalparameters. In some embodiments, manual entering of data is not requiredin order to derive calorie burn; however, data entry may be used toimprove the accuracy of the results. In some embodiments, if a user hasforgotten to enter a new weight, he/she can enter it for past weeks andthe calorie burn may be updated accordingly.

The sensors 202 may include one or more sensors for activitymeasurement. In some embodiments, the system may include one or moremulti-axes accelerometers and/or gyroscope to provide a measurement ofactivity. In some embodiments, the accelerometer may further be used tofilter a signal from the optical sensor for measuring heart rate and toprovide a more accurate measurement of the heart rate. In someembodiments, the wearable system may include a multi-axis accelerometerto measure motion and calculate distance, whether it be in real terms assteps or miles or as a converted number. Activity sensors may be used,for example, to classify or categorize activity, such as walking,running, performing another sport, standing, sitting or lying down. Insome embodiments, one or more of collected physiological data may beaggregated to generate an aggregate activity level. For example, heartrate, calorie burn, and distance may be used to derive an aggregateactivity level. The aggregate level may be compared with or evaluatedrelative to previous recordings of the user's aggregate activity level,as well as the aggregate activity levels of other users.

The sensors 202 may include a thermometer for monitoring the user's bodyor skin temperature. In one embodiment, the sensors may be used torecognize sleep based on a temperature drop, GSR data, lack of activityaccording to data collected by the accelerometer, and reduced heart rateas measured by the heart rate monitor. The body temperature, inconjunction with heart rate monitoring and motion, may be used tointerpret whether a user is sleeping or just resting, as bodytemperature drops significantly when an individual is about to fallasleep), and how well an individual is sleeping as motion indicates alower quality of sleep. The body temperature may also be used todetermine whether the user is exercising and to categorize and/oranalyze activities.

The system 200 includes one or more batteries 204. According to oneembodiment, the one or more batteries may be configured to allowcontinuous wear and usage of the wearable system. In one embodiment, thewearable system may include two or more batteries. The system mayinclude a removable battery that may be recharged using a charger. Inone example, the removable battery may be configured to slip in and outof a head portion of the system, attach onto the bracelet, or the like.In one example, the removable battery may be able to power the systemfor around a week. Additionally, the system may include a built-inbattery. The built-in battery may be recharged by the removable battery.The built-in battery may be configured to power the bracelet for arounda day on its own. When the more removable battery is being charged, theuser does not need to remove the system and may continue collecting datausing the built-in battery. In other embodiments, the two batteries mayboth be removable and rechargeable.

In some embodiments, the system 200 may include a battery that is awireless rechargeable battery. For example, the battery may be rechargedby placing the system or the battery on a rechargeable mat. In otherexample, the battery may be a long range wireless rechargeable battery.In other embodiments, the battery may be a rechargeable via motion. Inyet other embodiments, the battery may be rechargeable using a solarenergy source.

The wearable system 200 includes one or more non-transitorycomputer-readable media 206 for storing raw data detected by the sensorsof the system and processed data calculated by a processing module ofthe system.

The system 200 includes a processor 208, a memory 210, a bus 212, anetwork interface 214, and an interface 216. The network interface 214is configured to wirelessly communicate data to an external network 218.The network 218 may include any communication network through whichcomputer systems may exchange data. For example, the network 218 mayinclude, but is not limited to, the Internet, an intranet, a LAN (LocalArea Network), a WAN (Wide Area Network), a MAN (Metropolitan AreaNetwork), a wireless network, an optical network, and the like. Toexchange data via the network 218, the system 200 and the network 218may use various methods, protocols and standards including, but notlimited to, token ring, Ethernet, wireless Ethernet, Bluetooth, TCP/IP,UDP, HTTP, FTP, SNMP, SMS, MMS, SS7, JSON, XML, REST, SOAP, CORBA, HOP,RMI, DCOM and Web Services. To ensure data transfer is secure, thesystem 200 may transmit data via the network using a variety of securitymeasures including, but not limited to, TSL, SSL and VPN.

Some embodiments of the wearable system may be configured to streaminformation wirelessly to a social network. In some embodiments, datastreamed from a user's wearable system to an external network 218 may beaccessed by the user via a website. The network interface may beconfigured such that data collected by the system may be streamedwirelessly. In some embodiments, data may be transmitted automatically,without the need to manually press any buttons. In some embodiments, thesystem may include a cellular chip built into the system. In oneexample, the network interface may be configured to stream data usingBluetooth technology. In another example, the network interface may beconfigured to stream data using a cellular data service, such as via a3G or 4G cellular network.

The system 200 may be coupled to one or more servers 220 via acommunication network 218.

In some embodiments, a physiological measurement system may beconfigured in a modular design to enable continuous operation of thesystem in monitoring physiological information of a user wearing thesystem. The module design may include a strap and a separate modularhead portion or housing that is removably couplable to the strap.

In the non-limiting illustrative module design, the strap 102 of aphysiological measurement system may be provided with a set ofcomponents that enables continuous monitoring of at least a heart rateof the user so that it is independent and fully self-sufficient incontinuously monitoring the heart rate without requiring the modularhead portion 104. In one embodiment, the strap includes a plurality oflight emitters for emitting light toward the user's skin, a plurality oflight detectors for receiving light reflected from the user's skin, anelectronic circuit board comprising a plurality of electronic componentsconfigured for analyzing data corresponding to the reflected light toautomatically and continually determine a heart rate of the user, and afirst set of one or more batteries for supplying electrical power to thelight emitters, the light detectors and the electronic circuit board. Insome embodiments, the strap may also detect one or more otherphysiological characteristics of the user including, but not limited to,temperature, galvanic skin response, and the like.

Certain exemplary systems may be configured to be coupled to any desiredpart of a user's body so that the system may be moved from one portionof the body (e.g., wrist) to another portion of the body (e.g., ankle)without affecting its function and operation. In one embodiment, theidentity of the portion of the user's body to which the wearable systemis attached may be determined based on one or more parameters including,but not limited to, absorbance level of light as returned from theuser's skin, reflectance level of light as returned from the user'sskin, motion sensor data (e.g., accelerometer and/or gyroscope),altitude of the wearable system, and the like.

In some embodiments, the processing module is configured to determinethat the wearable system is taken off from the user's body. In oneexample, the processing module may determine that the wearable systemhas been taken off if data from the galvanic skin response sensorindicates data atypical of a user's skin. If the wearable system isdetermined to be taken off from the user's body, the processing moduleis configured to deactivate the light emitters and the light detectorsand cease monitoring of the heart rate of the user to conserve power.

Exemplary systems include a processing module configured to filter theraw photoplethysmography data received from the light detectors tominimize contributions due to motion, and subsequently process thefiltered data to detect peaks in the data that correspond with heartbeats of a user. The overall algorithm for detecting heart beats takesas input the analog signals from optical sensors (mV) and accelerometer,and outputs an implied beats per minute (heart rate) of the signalaccurate within a few beats per minute as that determined by anelectrocardiography machine even during motion.

In one aspect, using multiple LEDs with different wavelengths reactingto movement in different ways can allow for signal recovery withstandard signal processing techniques. The availability of accelerometerinformation can also be used to compensate for coarse movement signalcorruption. In order to increase the range of movements that thealgorithm can successfully filter out, an aspect utilizes techniquesthat augment the algorithm already in place. For example, filteringviolent movements of the arm during very short periods of time, such asboxing as exercising, may be utilized by the system. By selectivesampling and interpolating over these impulses, an aspect can accountfor more extreme cases of motion. Additionally, an investigation intodifferent LED wavelengths, intensities, and configurations can allow thesystems described herein to extract a signal across a wide spectrum ofskin types and wrist sizes. In other words, motion filtering algorithmsand signal processing techniques may assist in mitigating the riskcaused by movement.

FIG. 3 is a flowchart illustrating an exemplary signal processingalgorithm for generating a sequence of heart rates for every detectedheartbeat that is embodied in computer-executable instructions stored onone or more non-transitory computer-readable media. In step 302, lightemitters of a wearable physiological measurement system emit lighttoward a user's skin. In step 304, light reflected from the user's skinis detected at the light detectors in the system. In step 306, signalsor data associated with the reflected light are pre-processed using anysuitable technique to facilitate detection of heart beats. In step 308,a processing module of the system executes one or morecomputer-executable instructions associated with a peak detectionalgorithm to process data corresponding to the reflected light to detecta plurality of peaks associated with a plurality of beats of the user'sheart. In step 310, the processing module determines an RR intervalbased on the plurality of peaks detected by the peak detectionalgorithm. In step 312, the processing module determines a confidencelevel associated with the RR interval.

Based on the confidence level associated with the RR interval estimate,the processing module selects either the peak detection algorithm or afrequency analysis algorithm to process data corresponding to thereflected light to determine the sequence of instantaneous heart ratesof the user. The frequency analysis algorithm may process the datacorresponding to the reflected light based on the motion of the userdetected using, for example, an accelerometer. The processing module mayselect the peak detection algorithm or the frequency analysis algorithmregardless of a motion status of the user. It is advantageous to use theconfidence in the estimate in deciding whether to switch tofrequency-based methods as certain frequency-based approaches are unableto obtain accurate RR intervals for heart rate variability analysis.Therefore, an implementation maintains the ability to obtain the RRintervals for as long as possible, even in the case of motion, therebymaximizing the information that can be extracted.

For example, in step 314, it is determined whether the confidence levelassociated with the RR interval is above (or equal to or above) athreshold. In certain embodiments, the threshold may be predefined, forexample, about 50%-90% in some embodiments and about 80% in onenon-limiting embodiment. In other embodiments, the threshold may beadaptive, i.e., the threshold may be dynamically and automaticallydetermined based on previous confidence levels. For example, if one ormore previous confidence levels were high (i.e., above a certain level),the system may determine that a present confidence level that isrelatively low compared to the previous levels is indicative of a lessreliable signal. In this case, the threshold may be dynamically adjustedto be higher so that a frequency-based analysis method may be selectedto process the less reliable signal.

If the confidence level is above (or equal to or above) the threshold,in step 316, the processing module may use the plurality of peaks todetermine an instantaneous heart rate of the user. On the other hand, instep 320, based on a determination that the confidence level associatedwith the RR interval is equal to or below the predetermined threshold,the processing module may execute one or more computer-executableinstructions associated with the frequency analysis algorithm todetermine an instantaneous heart rate of the user. The confidencethreshold may be dynamically set based on previous confidence levels.

In some embodiments, in steps 318 or 322, the processing moduledetermines a heart rate variability of the user based on the sequence ofthe instantaneous heart rates/beats.

The system may include a display device configured to render a userinterface for displaying the sequence of the instantaneous heart ratesof the user, the RR intervals and/or the heart rate variabilitydetermined by the processing module. The system may include a storagedevice configured to store the sequence of the instantaneous heartrates, the RR intervals and/or the heart rate variability determined bythe processing module.

In one aspect, the system may switch between different analyticaltechniques for determining a heart rate such as a statistical techniquefor detecting a heart rate and a frequency domain technique fordetecting a heart rate. These two different modes have differentadvantages in terms of accuracy, processing efficiency, and informationcontent, and as such may be useful at different times and underdifferent conditions. Rather than selecting one such mode or techniqueas an attempted optimization, the system may usefully switch back andforth between these differing techniques, or other analyticaltechniques, using a predetermined criterion. An exemplary statisticaltechnique employs probabilistic peak detection. An exemplary frequencyanalysis algorithm used in an implementation isolates the highestfrequency components of the optical data, checks for harmonics common inboth the accelerometer data and the optical data, and performs filteringof the optical data. This latter algorithm may, for example, take asinput raw analog signals from the accelerometer (3-axis) and pulsesensors, and output heart rate values or beats per minute (BPM) for agiven period of time related to the window of the spectrogram.

The exemplary wearable system computes heart rate variability (HRV) toobtain an understanding of the recovery status of the body. These valuesare captured right before a user awakes or when the user is not moving,in both cases photoplethysmography (PPG) variability yieldingequivalence to the ECG HRV. HRV is traditionally measured using an ECGmachine and obtaining a time series of R-R intervals. Because anexemplary wearable system utilizes photoplethysmography (PPG), it doesnot obtain the electric signature from the heart beats; instead, thepeaks in the obtained signal correspond to arterial blood volume. Atrest, these peaks are directly correlated with cardiac cycles, whichenables the calculation of HRV via analyzing peak-to-peak intervals (thePPG analog of RR intervals). It has been demonstrated in medicalliterature that these peak-to-peak intervals, the “PPG variability,” isidentical to ECG HRV while at rest.

An exemplary system may include a processing module that is configuredto automatically adjust one or more operational characteristics of thelight emitters and/or the light detectors to minimize power consumptionwhile ensuring that all heart beats of the user are reliably andcontinuously detected. The operational characteristics may include, butare not limited to, a frequency of light emitted by the light emitters,the number of light emitters activated, a duty cycle of the lightemitters, a brightness of the light emitters, a sampling rate of thelight detectors, and the like. The processing module may adjust theoperational characteristics based on one or more signals or indicatorsobtained or derived from one or more sensors in the system including,but not limited to, a motion status of the user, a sleep status of theuser, historical information on the user's physiological and/or habits,an environmental or contextual condition (e.g., ambient lightconditions), a physical characteristic of the user (e.g., the opticalcharacteristics of the user's skin), and the like.

In one embodiment, the processing module may receive data on the motionof the user using, for example, an accelerometer. The processing modulemay process the motion data to determine a motion status of the userwhich indicates the level of motion of the user, for example, exercise,light motion (e.g., walking), no motion or rest, sleep, and the like.The processing module may adjust the duty cycle of one or more lightemitters and the corresponding sampling rate of the one or more lightdetectors based on the motion status. For example, light emitters forPPG may be activated at a duty cycle ranging from about 1% to about100%. In another example, the light emitters may be activated at a dutycycle ranging from about 20% to about 50% to minimize power consumption.Certain exemplary sampling rates of the light detectors may range fromabout 50 Hz to about 1000 Hz, but are not limited to these exemplaryrates. Certain non-limiting sampling rates are, for example, about 100Hz, 200 Hz, 500 Hz, and the like.

In one non-limiting example, the light detectors may sample continuouslywhen the user is performing an exercise routine so that the errorstandard deviation is kept within 5 beats per minute (BPM). When theuser is at rest, the light detectors may be activated for about a 1%duty cycle—10 milliseconds each second (i.e., 1% of the time) so thatthe error standard deviation is kept within 5 BPM (including an errorstandard deviation in the heart rate measurement of 2 BPM and an errorstandard deviation in the heart rate changes between measurement of 3BPM). When the user is in light motion (e.g., walking), the lightdetectors may be activated for about a 10% duty cycle—100 millisecondseach second (i.e., 10% of the time) so that the error standard deviationis kept within 6 BPM (including an error standard deviation in the heartrate measurement of 2 BPM and an error standard deviation in the heartrate changes between measurement of 4 BPM).

The processing module may adjust the brightness of one or more lightemitters by adjusting the current supplied to the light emitters. Forexample, a first level of brightness may be set by current rangingbetween about 1 mA to about 10 mA, but is not limited to this exemplaryrange. A second higher level of brightness may be set by current rangingfrom about 11 mA to about 30 mA, but is not limited to this exemplaryrange. A third higher level of brightness may be set by current rangingfrom about 80 mA to about 120 mA, but is not limited to this exemplaryrange. In one non-limiting example, first, second and third levels ofbrightness may be set by current of about 5 mA, about 20 mA and about100 mA, respectively.

Shorter-wavelength LEDs may require more power than is required by othertypes of heart rate sensors, such as, a piezo-sensor or an infraredsensor. Therefore, an exemplary wearable system may provide and use aunique combination of sensors—one or more light detectors for periodswhere motion is expected and one or more piezo and/or infrared sensorsfor low motion periods (e.g., sleep)—to save battery life. Certain otherembodiments of a wearable system may exclude piezo-sensors and/orinfrared sensors.

For example, upon determining that the motion status indicates that theuser is at a first higher level of motion (e.g., exercising), one ormore light emitters may be activated to emit light at a firstwavelength. Upon determining that the motion status indicates that theuser is at a second lower level of motion (e.g., at rest), non-lightbased sensors may be activated. The threshold levels of motion thattrigger adjustment of the type of sensor may be based on one or morefactors including, but are not limited to, skin properties, ambientlight conditions, and the like.

The system may determine the type of sensor to use at a given time basedon the level of motion (e.g., via an accelerometer) and whether the useris asleep (e.g., based on movement input, skin temperature and heartrate). Based on a combination of these factors the system selectivelychooses which type of sensor to use in monitoring the heart rate of theuser. Common symptoms of being asleep are periods of no movement orsmall bursts of movement (such as shifting in bed), lower skintemperature (although it is not a dramatic drop from normal), drasticGSR changes, and heart rate that is below the typical resting heart ratewhen the user is awake. These variables depend on the physiology of aperson and thus a machine learning algorithm is trained withuser-specific input to determine when he/she is awake/asleep anddetermine from that the exact parameters that cause the algorithm todeem someone asleep.

In an exemplary configuration, the light detectors may be positioned onthe underside of the wearable system and all of the heart rate sensorsmay be positioned adjacent to each other. For example, the low powersensor(s) may be adjacent to the high power sensor(s) as the sensors maybe chosen and placed where the strongest signal occurs. In one exampleconfiguration, a 3-axis accelerometer may be used that is located on thetop part of the wearable system. In some embodiments, an operationalcharacteristic of the microprocessor may be automatically adjusted tominimize power consumption. This adjustment may be based on a level ofmotion of the user's body.

More generally, the above description contemplates a variety oftechniques for sensing conditions relating to heart rate monitoring orrelated physiological activity either directly (e.g., confidence levelsor accuracy of calculated heart rate) or indirectly (e.g., motiondetection, temperature). However measured, these sensed conditions canbe used to intelligently select from among a number of different modes,including hardware modes, software modes, and combinations of theforegoing, for monitoring heart rate based on, e.g., accuracy, powerusage, detected activity states, and so forth. Thus there is disclosedherein techniques for selecting from among two or more different heartrate monitoring modes according to a sensed condition.

Exemplary embodiments provide an analytics system for providingqualitative and quantitative monitoring of a user's body, health andphysical training. The analytics system is implemented incomputer-executable instructions encoded on one or more non-transitorycomputer-readable media. The analytics system relies on and usescontinuous data on one or more physiological parameters including, butnot limited to, heart rate. The continuous data used by the analyticssystem may be obtained or derived from an exemplary physiologicalmeasurement system disclosed herein, or may be obtained or derived froma derived source or system, for example, a database of physiologicaldata. In some embodiments, the analytics system computes, stores anddisplays one or more indicators or scores relating to the user's body,health and physical training including, but not limited to, an intensityscore and a recovery score. The scores may be updated in real-time andcontinuously or at specific time periods, for example, the recoveryscore may be determined every morning upon waking up, the intensityscore may be determined in real-time or after a workout routine or foran entire day.

In certain exemplary embodiments, a fitness score may be automaticallydetermined based on the physiological data of two or more users ofexemplary wearable systems.

An intensity score or indicator provides an accurate indication of thecardiovascular intensities experienced by the user during a portion of aday, during the entire day or during any desired period of time (e.g.,during a week or month). The intensity score is customized and adaptedfor the unique physiological properties of the user and takes intoaccount, for example, the user's age, gender, anaerobic threshold,resting heart rate, maximum heart rate, and the like. If determined foran exercise routine, the intensity score provides an indication of thecardiovascular intensities experienced by the user continuouslythroughout the routine. If determined for a period of including andbeyond an exercise routine, the intensity score provides an indicationof the cardiovascular intensities experienced by the user during theroutine and also the activities the user performed after the routine(e.g., resting on the couch, active day of shopping) that may affecttheir recovery or exercise readiness.

In exemplary embodiments, the intensity score is calculated based on theuser's heart rate reserve (HRR) as detected continuously throughout thedesired time period, for example, throughout the entire day. In oneembodiment, the intensity score is an integral sum of the weighted HRRdetected continuously throughout the desired time period. FIG. 4 is aflowchart illustrating an exemplary method of determining an intensityscore.

In step 402, continuous heart rate readings are converted to HRR values.A time series of heart rate data used in step 402 may be denoted as:HεT

A time series of HRR measurements, v(t), may be defined in the followingexpression in which MHR is the maximum heart rate and RHR is the restingheart rate of the user:

${v(t)} = \frac{{H(t)} - {RHR}}{{MHR} - {RHR}}$

In step 404, the HRR values are weighted according to a suitableweighting scheme. Cardiovascular intensity, indicated by an intensityscore, is defined in the following expression in which w is a weightingfunction of the HRR measurements:l(t ₀ ,t ₁)=∫_(t) ₀ ^(t) ¹ w(v(t))dt

In step 406, the weighted time series of HRR values is summed andnormalized.l _(t)=∫_(T) w(v(t))dt≦w(1)|T|

Thus, the weighted sum is normalized to the unit interval, i.e., [0, 1]

$N_{T} = \frac{I_{T}}{{{w(1)} \cdot 24}\mspace{14mu}{hr}}$

In step 408, the summed and normalized values are scaled to generateuser-friendly intensity score values. That is, the unit interval istransformed to have any desired distribution in a scale (e.g., a scaleincluding 21 points from 0 to 21), for example, arctangent, sigmoid,sinusoidal, and the like. In certain distributions, the intensity valuesincrease at a linear rate along the scale, and in others, at the highestranges the intensity values increase at more than a linear rate toindicate that it is more difficult to climb in the scale toward theextreme end of the scale. In some embodiments, the raw intensity scoresare scaled by fitting a curve to a selected group of “canonical”exercise routines that are predefined to have particular intensityscores.

In one embodiment, monotonic transformations of the unit interval areachieved to transform the raw HRR values to user-friendly intensityscores. An exemplary scaling scheme, expressed as ƒ: [0, 1]

[0, 1], is performed using the following function:

$\left( {x,N,p} \right) = {0.5\left( {\frac{\arctan\left( {N\left( {x - p} \right)} \right)}{\text{π/2}} + 1} \right)}$

To generate an intensity score, the resulting value may be multiplied bya number based on the desired scale of the intensity score. For example,if the intensity score is graduated from zero to 21, then the value maybe multiplied by 21.

In step 410, the intensity score values are stored on a non-transitorystorage medium for retrieval, display and usage. In step 412, theintensity score values are, in some embodiments, displayed on a userinterface rendered on a visual display device. The intensity scorevalues may be displayed as numbers and/or with the aid of graphicaltools, e.g., a graphical display of the scale of intensity scores withcurrent score, and the like. In some embodiments, the intensity scoremay be indicated by audio. In step 412, the intensity score values are,in some embodiments, displayed along with one or more quantitative orqualitative pieces of information on the user including, but not limitedto, whether the user has exceeded his/her anaerobic threshold, the heartrate zones experienced by the user during an exercise routine, howdifficult an exercise routine was in the context of the user's training,the user's perceived exertion during an exercise routine, whether theexercise regimen of the user should be automatically adjusted (e.g.,made easier if the intensity scores are consistently high), whether theuser is likely to experience soreness the next day and the level ofexpected soreness, characteristics of the exercise routine (e.g., howdifficult it was for the user, whether the exercise was in bursts oractivity, whether the exercise was tapering, etc.), and the like. In oneembodiment, the analytics system may automatically generate, store anddisplay an exercise regimen customized based on the intensity scores ofthe user.

Step 406 may use any of a number of exemplary static or dynamicweighting schemes that enable the intensity score to be customized andadapted for the unique physiological properties of the user. In oneexemplary static weighting scheme, the weights applied to the HRR valuesare based on static models of a physiological process. The human bodyemploys different sources of energy with varying efficiencies andadvantages at different HRR levels. For example, at the anaerobicthreshold (AT), the body shifts to anaerobic respiration in which thecells produce two adenosine triphosphate (ATP) molecules per glucosemolecule, as opposed to 36 at lower HRR levels. At even higher HRRlevels, there is a further subsequent threshold (CPT) at which creatinetriphosphate (CTP) is employed for respiration with even lessefficiency.

In order to account for the differing levels of cardiovascular exertionand efficiency at the different HRR levels, in one embodiment, thepossible values of HRR are divided into a plurality of categories,sections or levels (e.g., three) dependent on the efficiency of cellularrespiration at the respective categories. The HRR parameter range may bedivided in any suitable manner, such as, piecewise, includingpiecewise-linear, piecewise-exponential, and the like. An exemplarypiecewise-linear division of the HRR parameter range enables weightingeach category with strictly increasing values. This scheme captures anaccurate indication of the cardiovascular intensity experienced by theuser because it is more difficult to spend time at higher HRR values,which suggests that the weighting function should increase at theincreasing weight categories.

In one non-limiting example, the HRR parameter range may be considered arange from zero (0) to one (1) and divided into categories with strictlyincreasing weights. In one example, the HRR parameter range may bedivided into a first category of a zero HRR value and may assign thiscategory a weight of zero; a second category of HRR values fallingbetween zero (0) and the user's anaerobic threshold (AT) and may assignthis category a weight of one (1); a third category of HRR valuesfalling between the user's anaerobic threshold (AT) and a threshold atwhich the user's body employs creatine triphosphate for respiration(CPT) and may assign this category a weight of 18; and a fourth categoryof HRR values falling between the creatine triphosphate threshold (CPT)and one (1) and may assign this category a weight of 42, although othernumbers of HRR categories and different weight values are possible. Thatis, in this example, the weights are defined as:

${w(v)} = \left\{ \begin{matrix}\text{0:} & {v = 0} \\\text{1:} & {v \in \left( {0,{AT}} \right\rbrack} \\\text{18:} & {v \in \left( {{AT},{CPT}} \right\rbrack} \\\text{42:} & {v \in \left( {{CPT},1} \right\rbrack}\end{matrix} \right.$

In another exemplary embodiment of the weighting scheme, the HRR timeseries is weighted iteratively based on the intensity scores determinedthus far (e.g., the intensity score accrued thus far) and the path takenby the HRR values to get to the present intensity score. In anotherexemplary embodiment of the weighting scheme, a predictive approach isused by modeling the weights or coefficients to be the coefficientestimates of a logistic regression model. One of ordinary skill in theart will recognize that two or more aspects of any of the disclosedweighting schemes may be applied separately or in combination in anexemplary method for determining an intensity score.

In one aspect, heart rate zones quantify the intensity of workouts byweighing and comparing different levels of heart activity as percentagesof maximum heart rate. Analysis of the amount of time an individualspends training at a certain percentage of his/her MHR may revealhis/her state of physical exertion during a workout. This intensity,developed from the heart rate zone analysis, motion, and activity, maythen indicate his/her need for rest and recovery after the workout,e.g., to minimize delayed onset muscle soreness (DOMS) and preparehim/her for further activity. As discussed above, MHR, heart rate zones,time spent above the anaerobic threshold, and HRV in RSA (RespiratorySinus Arrhythmia) regions—as well as personal information (gender, age,height, weight, etc.) may be utilized in data processing.

A recovery score or indicator provides an accurate indication of thelevel of recovery of a user's body and health after a period of physicalexertion. The human autonomic nervous system controls the involuntaryaspects of the body's physiology and is typically subdivided into twobranches: parasympathetic (deactivating) and sympathetic (activating).Heart rate variability (HRV), i.e., the fluctuation in inter-heartbeatinterval time, is a commonly studied result of the interplay betweenthese two competing branches. Parasympathetic activation reflects inputsfrom internal organs, causing a decrease in heart rate. Sympatheticactivation increases in response to stress, exercise and disease,causing an increase in heart rate. For example, when high intensityexercise takes place, the sympathetic response to the exercise persistslong after the completion of the exercise. When high intensity exerciseis followed by insufficient recovery, this imbalance lasts typicallyuntil the next morning, resulting in a low morning HRV. This resultshould be taken as a warning sign as it indicates that theparasympathetic system was suppressed throughout the night. Whilesuppressed, normal repair and maintenance processes that ordinarilywould occur during sleep were suppressed as well. Suppression of thenormal repair and maintenance processes results in an unprepared statefor the next day, making subsequent exercise attempts more challenging.

The recovery score is customized and adapted for the uniquephysiological properties of the user and takes into account, forexample, the user's heart rate variability (HRV), resting heart rate,sleep quality and recent physiological strain (indicated, in oneexample, by the intensity score of the user). In one exemplaryembodiment, the recovery score is a weighted combination of the user'sheart rate variability (HRV), resting heart rate, sleep qualityindicated by a sleep score, and recent strain (indicated, in oneexample, by the intensity score of the user). In an exemplar, the sleepscore combined with performance readiness measures (such as, morningheart rate and morning heart rate variability) provides a completeoverview of recovery to the user. By considering sleep and HRV alone orin combination, the user can understand how exercise-ready he/she iseach day and to understand how he/she arrived at the exercise-readinessscore each day, for example, whether a low exercise-readiness score is apredictor of poor recovery habits or an inappropriate training schedule.This insight aids the user in adjusting his/her daily activities,exercise regimen and sleeping schedule therefore obtain the most out ofhis/her training.

In some cases, the recovery score may take into account perceivedpsychological strain experienced by the user. In some cases, perceivedpsychological strain may be detected from user input via, for example, aquestionnaire on a mobile device or web application. In other cases,psychological strain may be determined automatically by detectingchanges in sympathetic activation based on one or more parametersincluding, but not limited to, heart rate variability, heart rate,galvanic skin response, and the like.

With regard to the user's HRV used in determining the recovery score,suitable techniques for analyzing HRV include, but are not limited to,time-domain methods, frequency-domain methods, geometric methods andnon-linear methods. In one embodiment, the HRV metric of theroot-mean-square of successive differences (RMSSD) of RR intervals isused. The analytics system may consider the magnitude of the differencesbetween 7-day moving averages and 3-day moving averages of thesereadings for a given day. Other embodiments may use Poincaré Plotanalysis or other suitable metrics of HRV.

The recovery score algorithm may take into account RHR along withhistory of past intensity and recovery scores.

With regard to the user's resting heart rate, moving averages of theresting heart rate are analyzed to determine significant deviations.Consideration of the moving averages is important since day-to-dayphysiological variation is quite large even in healthy individuals.Therefore, the analytics system may perform a smoothing operation todistinguish changes from normal fluctuations.

Although an inactive condition, sleep is a highly active recovery stateduring which a major portion of the physiological recovery process takesplace. Nonetheless, a small, yet significant, amount of recovery canoccur throughout the day by rehydration, macronutrient replacement,lactic acid removal, glycogen re-synthesis, growth hormone productionand a limited amount of musculoskeletal repair. In assessing the user'ssleep quality, the analytics system generates a sleep score usingcontinuous data collected by an exemplary physiological measurementsystem regarding the user's heart rate, skin conductivity, ambienttemperature and accelerometer/gyroscope data throughout the user'ssleep. Collection and use of these four streams of data enable anunderstanding of sleep previously only accessible through invasive anddisruptive over-night laboratory testing. For example, an increase inskin conductivity when ambient temperature is not increasing, thewearer's heart rate is low, and the accelerometer/gyroscope shows littlemotion, may indicate that the wearer has fallen asleep. The sleep scoreindicates and is a measure of sleep efficiency (how good the user'ssleep was) and sleep duration (if the user had sufficient sleep). Eachof these measures is determined by a combination of physiologicalparameters, personal habits and daily stress/strain (intensity) inputs.The actual data measuring the time spent in various stages of sleep maybe combined with the wearer's recent daily history and a longer-termdata set describing the wearer's personal habits to assess the level ofsleep sufficiency achieved by the user. The sleep score is designed tomodel sleep quality in the context of sleep duration and history. Itthus takes advantage of the continuous monitoring nature of theexemplary physiological measurement systems disclosed herein byconsidering each sleep period in the context of biologically-determinedsleep needs, pattern-determined sleep needs and historically-determinedsleep debt.

The recovery and sleep score values are stored on a non-transitorystorage medium for retrieval, display and usage. The recovery and/orsleep score values are, in some embodiments, displayed on a userinterface rendered on a visual display device. The recovery and/or sleepscore values may be displayed as numbers and/or with the aid ofgraphical tools, e.g., a graphical display of the scale of recoveryscores with current score, and the like. In some embodiments, therecovery and/or sleep score may be indicated by audio. The recoveryscore values are, in some embodiments, displayed along with one or morequantitative or qualitative pieces of information on the user including,but not limited to, whether the user has recovered sufficiently, whatlevel of activity the user is prepared to perform, whether the user isprepared to perform an exercise routine a particular desired intensity,whether the user should rest and the duration of recommended rest,whether the exercise regimen of the user should be automaticallyadjusted (e.g., made easier if the recovery score is low), and the like.In one embodiment, the analytics system may automatically generate,store and display an exercise regimen customized based on the recoveryscores of the user alone or in combination with the intensity scores.

As discussed above, the sleep performance metric may be based onparameters like the number of hours of sleep, sleep onset latency, andthe number of sleep disturbances. In this manner, the score may comparea tactical athlete's duration and quality of sleep in relation to thetactical athlete's evolving sleep need (e.g., a number of hours based onrecent strain, habitual sleep need, signs of sickness, and sleep debt).By way of example, a soldier may have a dynamically changing need forsleep, and it may be important to consider the total hours of sleep inrelation to the amount of sleep that may have been required. Byproviding an accurate sensor for sleep and sleep performance, an aspectmay evaluate sleep in the context of the overall day and lifestyle of aspecific user.

FIG. 5 is a flowchart illustrating an exemplary method by which a usermay use intensity and recovery scores. In step 502, the wearablephysiological measurement system begins determining heart ratevariability (HRV) measurements based on continuous heart rate datacollected by an exemplary physiological measurement system. In somecases, it may take the collection of several days of heart rate data toobtain an accurate baseline for the HRV. In step 504, the analyticssystem may generate and display intensity score for an entire day or anexercise routine. In some cases, the analytics system may displayquantitative and/or qualitative information corresponding to theintensity score.

In step 506, in an exemplary embodiment, the analytics system mayautomatically generate or adjust an exercise routine or regimen based onthe user's actual intensity scores or desired intensity scores. Forexample, based on inputs of the user's actual intensity scores, adesired intensity score (that is higher than the actual intensityscores) and a first exercise routine currently performed by the user(e.g., walking), the analytics system may recommend a second differentexercise routine that is typically associated with higher intensityscores than the first exercise routine (e.g., running).

In step 508, at any given time during the day (e.g., every morning), theanalytics system may generate and display a recovery score. In somecases, the analytics system may display quantitative and/or qualitativeinformation corresponding to the intensity score. For example, in step510, in an exemplary embodiment, the analytics system may determine ifthe recovery is greater than (or equal to or greater than) a firstpredetermined threshold (e.g., about 60% to about 80% in some examples)that indicates that the user is recovered and is ready for exercise. Ifthis is the case, in step 512, the analytics system may indicate thatthe user is ready to perform an exercise routine at a desired intensityor that the user is ready to perform an exercise routine morechallenging than the past day's routine. Otherwise, in step 514, theanalytics system may determine if the recovery is lower than (or equalto or lower than) a second predetermined threshold (e.g., about 10% toabout 40% in some examples) that indicates that the user has notrecovered. If this is the case, in step 516, the analytics system mayindicate that the user should not exercise and should rest for anextended period. The analytics system may, in some cases, the durationof recommended rest. Otherwise, in step 518, the analytics system mayindicate that the user may exercise according to his/her exerciseregimen while being careful not to overexert him/herself. The thresholdsmay, in some cases, be adjusted based on a desired intensity at whichthe user desires to exercise. For example, the thresholds may beincreased for higher planned intensity scores.

FIG. 6 is a flow chart illustrating a method for detecting heart ratevariability in sleep states. The method 600 may be used in cooperationwith any of the devices, systems, and methods described herein, such asby operating a wearable, continuous physiological monitoring device toperform the following steps. The wearable, continuous physiologicalmonitoring system may for example include a processor, one or more lightemitting diodes, one or more light detectors configured to obtain heartrate data from a user, and one or more other sensors to assist indetecting stages of sleep. In general, the method 600 aims to measureheart rate variability in the last phase of sleep before waking in orderto provide a consistent and accurate basis for calculating a physicalrecovery score.

As shown in step 602, the method 600 may include detecting a sleep stateof a user. This may, for example, include any form of continuous orperiodic monitoring of sleep states using any of a variety of sensors oralgorithms as generally described herein.

Sleep states (also be referred to as “sleep phases,” “sleep cycles,”“sleep stages,” or the like) may include rapid eye movement (REM) sleep,non-REM sleep, or any states/stages included therein. The sleep statesmay include different phases of non-REM sleep, including Stages 1-3.Stage 1 of non-REM sleep generally includes a state where a person'seyes are closed, but the person can be easily awakened; Stage 2 ofnon-REM sleep generally includes a state where a person is in lightsleep, i.e., where the person's heart rate slows and their bodytemperature drops in preparation for deeper sleep; and Stage 3 ofnon-REM sleep generally includes a state of deep sleep, where a personis not easily awakened. Stage 3 is often referred to as delta sleep,deep sleep, or slow wave sleep (i.e., from the high amplitude but smallfrequency brain waves typically found in this stage). Slow wave sleep isthought to be the most restful form of sleep, which relieves subjectivefeelings of sleepiness and restores the body.

REM sleep on the other hand typically occurs 1-2 hours after fallingasleep. REM sleep may include different periods, stages, or phases, allof which may be included within the sleep states that are detected asdescribed herein. During REM sleep, breathing may become more rapid,irregular and shallow, eyes may jerk rapidly (thus the term “Rapid EyeMovement” or “REM”), and limb muscles may be temporarily paralyzed.Brain waves during this stage typically increase to levels experiencedwhen a person is awake. Also, heart rate, cardiac pressure, cardiacoutput, and arterial pressure may become irregular when the body movesinto REM sleep. This is the sleep state in which most dreams occur, and,if awoken during REM sleep, a person can typically remember the dreams.Most people experience three to five intervals of REM sleep each night.

Homeostasis is the balance between sleeping and waking, and havingproper homeostasis may be beneficial to a person's health. Lack of sleepis commonly referred to as sleep deprivation, which tends to causeslower brain waves, a shorter attention span, heightened anxiety,impaired memory, mood disorders, and general mental, emotional, andphysical fatigue. Sleep debt (the effect of not getting enough sleep)may result in the diminished abilities to perform high-level cognitivefunctions. A person's circadian rhythms (i.e., biological processes thatdisplay an endogenous, entrainable oscillation of about 24 hours) may bea factor in a person's optimal amount of sleep. Thus, sleep may ingeneral be usefully monitored as a proxy for physical recovery. However,a person's heart rate variability at a particular moment duringsleep—during the last phase of sleep preceding a waking event—canfurther provide an accurate and consistent basis for objectivelycalculating a recovery score following a period of sleep.

According to the foregoing, sleep of a user may be monitored to detectvarious sleep states, transitions, and other sleep-related information.For example, the device may monitor/detect the duration of sleep states,the transitions between sleep states, the number of sleep cycles orparticular states, the number of transitions, the number of wakingevents, the transitions to an awake state, and so forth. Sleep statesmay be monitored and detected using a variety of strategies and sensorconfigurations according to the underlying physiological phenomena. Forexample, body temperature may be usefully correlated to various sleepstates and transitions. Similarly, galvanic skin response may becorrelated to sweating activity and various sleep states, any of whichmay also be monitored, e.g., with a galvanic skin response sensor, todetermine sleep states. Physical motion can also be easily monitoredusing accelerometers or the like, which can be used to detect waking orother activity involving physical motion. In another aspect, heart rateactivity itself may be used to infer various sleep states andtransitions, either alone or in combination with other sensor data.Other sensors may also or instead be used to monitor sleep activity,such as brain wave monitors, pupil monitors, and so forth, although theability to incorporate these types of detection into a continuouslywearable physiological monitoring device may be somewhat limiteddepending on the contemplated configuration.

As shown in step 604, the method 600 may include monitoring a heart rateof the user substantially continuously with the continuous physiologicalmonitoring system. Continuous heart rate monitoring is described abovein significant detail, and the description is not repeated here exceptto note generally that this may include raw sensor data, heart rate dataor peak data, and heart rate variability data over some historicalperiod that can be subsequently correlated to various sleep states andactivities.

As shown in step 606, the method 600 may include recording the heartrate as heart rate data. This may include storing the heart rate data inany raw or processed form on the device, or transmitting the data to alocal or remote location for storage. In one aspect, the data may bestored as peak-to-peak data or in some other semi-processed form withoutcalculating heart rate variability. This may be useful as a techniquefor conserving processing resources in a variety of contexts, forexample where only the heart rate variability at a particular time is ofinterest. Data may be logged in some unprocessed or semi-processed form,and then the heart rate variability at a particular point in time can becalculated once the relevant point in time has been identified.

As shown in step 610, the method 600 may include detecting a wakingevent at a transition from the sleep state of the user to an awakestate. It should be appreciated that the waking event may be a result ofa natural termination of sleep, e.g., after a full night's rest, or inresponse to an external stimulus that causes awakening prior tocompletion of a natural sleep cycle. Regardless of the precipitatingevent(s), the waking event may be detected via the various physiologicalchanges described above, or using any other suitable techniques. Whilethe emphasis herein is on a wearable, continuous monitoring device, itwill be understood that the device may also receive inputs from anexternal device such as a camera (for motion detection) or an infraredcamera (for body temperature detection) that can be used to aid inaccurately assessing various sleep states and transitions.

Thus the wearable, continuous physiological monitoring system maygenerally detect a waking event using one or more sensors including, forexample, one or more of an accelerometer, a galvanic skin responsesensor, a light sensor, and so forth. For example, in one aspect, thewaking event may be detected using a combination of motion data andheart rate data.

As shown in step 612, the method 600 may include calculating a heartrate variability of the user at a moment in a last phase of sleeppreceding the waking event based upon the heart rate data. While awaking event and a history of sleep states are helpful information forassessing recovery, the method 600 described herein specificallycontemplates use of the heart rate variability in a last phase of sleepas a consistent foundation for calculating recovery scores for a deviceuser. Thus, step 612 may also include detecting a slow wave sleep periodimmediately prior to the waking event, or otherwise determining the endof a slow wave or deep sleep episode immediately preceding the wakingevent.

It will be appreciated that the last phase of sleep preceding a naturalwaking event may be slow wave sleep. However, where a sleeper isawakened prematurely, this may instead include a last recorded episodeof REM sleep or some other phase of sleep immediately preceding thewaking event. This moment—the end of the last phase of sleep beforewaking—is the point at which heart rate variability data provides themost accurate and consistent indicator of physical recovery. Thus, withthe appropriate point of time identified, the historical heart rate data(in whatever form) may be used with the techniques described above tocalculate the corresponding heart rate variability. It will be furthernoted that the time period for this calculation may be selected withvarying degrees of granularity depending on the ability to accuratedetect the last phase of sleep and an end of the last phase of sleep.Thus for example, the time may be a predetermined amount of time beforewaking, or at the end of slow wave sleep, or some predetermined amountof time before the end of slow wave sleep is either detected orinferred. In another aspect, an average heart rate variability orsimilar metric may be determined for any number of discrete measurementswithin a window around the time of interest.

As shown in step 614, the method 600 may include calculating a durationof the sleep state. The quantity and quality of sleep may be highlyrelevant to physical recovery, and as such the duration of the sleepstate may be used to calculate a recovery score.

As shown in step 618, the method 600 may include evaluating a quality ofheart rate data using a data quality metric for a slow wave sleepperiod, e.g., the slow wave sleep period occurring most recently beforethe waking event. As noted above, the quality of heart rate measurementsmay vary over time for a variety of reasons. Thus the quality of heartrate data may be evaluated prior to selecting a particular moment orwindow of heart rate data for calculating heart rate variability, andthe method 600 may include using this quality data to select suitablevalues for calculating a recovery score. For example, the method 600 mayinclude calculating the heart rate variability for a window ofpredetermined duration within the slow wave sleep period having thehighest quality of heart rate data according to the data quality metric.

As shown in step 620, the method 600 may include calculating a recoveryscore for the user based upon the heart rate variability from the lastphase of sleep. The calculation may be based on other sources of data.For example, the calculation of recovery score may be based on theduration of sleep, the stages of sleep detected or informationconcerning the stages (e.g., amount of time in certain stages),information regarding the most recent slow wave sleep period or anothersleep period/state, information from the GSR sensor or other sensor(s),and so on. The method 600 may further include calculating additionalrecovery scores after one or more other waking events of the user forcomparison to the previously calculated recovery score. The actualcalculation of a discovery score is described in substantial detailabove, and this description is not repeated here except to note that theuse of a heart rate variability measurement from the last phase of sleepprovides an accurate and consistent basis for evaluating the physicalrecovery state of a user following a period of sleep.

As shown in step 630, the method 600 may include calculating a sleepscore and communicating this score to a user.

In one aspect, the sleep score may be a measure of prior sleepperformance. For example, a sleep performance score may quantify, on ascale of 0-100, the ratio of the hours of sleep during a particularresting period compared to the sleep needed. On this scale, if a usersleeps six hours and needed eight hours of sleep, then the sleepperformance may be calculated as 75%. The sleep performance score maybegin with one or more assumptions about needed sleep, based on, e.g.,age, gender, health, fitness level, habits, genetics, and so forth andmay be adapted to actual sleep patterns measured for an individual overtime.

The sleep score may also or instead include a sleep need score or otherobjective metric that estimates an amount of sleep needed by the user ofthe device in a next sleep period. In general, the score may be anysuitable quantitative representation including, e.g., a numerical valueover some predetermined scale (e.g., 0-10, 1-100, or any other suitablescale) or a representation of a number of hours of sleep that should betargeted by the user. In another aspect, the sleep score may becalculated as the number of additional hours of sleep needed beyond anormal amount of sleep for the user.

The score may be calculated using any suitable inputs that capture,e.g., a current sleep deficit, a measure of strain or exercise intensityover some predetermined prior interval, an accounting for any naps orother resting, and so forth. A variety of factors may affect the actualsleep need, including physiological attributes such as age, gender,health, genetics and so forth, as well as daytime activities, stress,napping, sleep deficit or deprivation, and so forth. The sleep deficitmay itself be based on prior sleep need and actual sleep performance(quality, duration, waking intervals, etc.) over some historical window.In one aspect, an objective scoring function for sleep need may have amodel of the form:SleepNeed=Baseline+ƒ₁(strain)+ƒ₂(debt)−Naps

In general, this calculation aims to estimate the ideal amount of sleepfor best rest and recovery during a next sleep period. When accountingfor time falling asleep, periods of brief wakefulness, and so forth, theactual time that should be dedicated to sleep may be somewhat higher,and this may be explicitly incorporated into the sleep need calculation,or left for a user to appropriately manage sleep habits.

In general, the baseline sleep may represent a standard amount of sleepneeded by the user on a typical rest day (e.g., with no strenuousexercise or workout). As noted above, this may depend on a variety offactors, and may be estimated or measured for a particular individual inany suitable manner. The strain component, ƒ₁(strain), may be assessedbased on a previous day's physical intensity, and will typicallyincrease the sleep need. Where intensity or strain is measured on anobjective scale from 0 to 21, the strain calculation may take thefollowing form, which yields an additional sleep time needed in minutesfor a strain, i:

${f(i)} = \frac{1.7}{1 + e^{\frac{17 - i}{3.5}}}$

The sleep debt, ƒ₂(debt), may generally measure a carryover of neededsleep that was not attained in a previous day. This may be scaled, andmay be capped at a maximum, according to individual sleepcharacteristics or general information about long term sleep deficit andrecovery. Naps may also be accounted for directly by correcting thesleep need for any naps that have been taken, or by calculating a napfactor that is scaled or otherwise manipulated or calculated to moreaccurately track the actual effect of naps on prospective sleep need.

However calculated, the sleep need may be communicated to a user, suchas by displaying a sleep need on a wrist-worn physiological monitoringdevice, or by sending an e-mail, text message or other alert to the userfor display on any suitable device.

FIG. 7 is a bottom view of a wearable, continuous physiologicalmonitoring device (the side facing a user's skin). As shown in thefigure, the wearable, continuous physiological monitoring system 700includes a wearable housing 702, one or more sensors 704, a processor706, and a light source 708.

The wearable housing 702 may be configured such that a user can wear acontinuous physiological monitoring device as part of the wearable,continuous physiological monitoring system 700. The wearable housing 702may be configured for cooperation with a strap or the like, e.g., forengagement with an appendage of a user.

The one or more sensors 704 may be disposed in the wearable housing 702.In one aspect, the one or more sensors 704 include a light detectorconfigured to provide data to the processor 706 for calculating a heartrate variability. The one or more sensors 704 may also or insteadinclude an accelerometer configured to provide data to the processor 706for detecting a sleep state or a waking event. In an implementation, theone or more sensors 704 measure a galvanic skin response of the user.

The processor 706 may be disposed in the wearable housing 702. Theprocessor 706 may be configured to operate the one or more sensors 704to detect a sleep state of a user wearing the wearable housing 702. Theprocessor 706 may be further configured to monitor a heart rate of theuser substantially continuously, and to record the heart rate as heartrate data without calculating a heart rate variability for the user. Theprocessor 706 may also or instead be configured to detect a waking eventat a transition from the sleep state of the user to an awake state, andto calculate the heart rate variability of the user at a moment in thelast phase of sleep preceding the waking event based upon the heart ratedata. The processor 706 may further be configured to calculate arecovery score for the user based upon the heart rate variability fromthe last phase of sleep.

The light source 708 may be coupled to the wearable housing 702 andcontrolled by the processor 706. The light source 708 may be directedtoward the skin of a user's appendage. Light from the light source 708may be detected by the one or more sensors 704.

Physiological signals acquired using the various different sensorsdescribed herein can be sensitive to conditions under which thephysiological signal is obtained. Thus, for example, the physiologicalsignal obtained during certain activities and/or under certainconditions can contain significant amounts of noise, or may havecharacteristics that vary according to the type of activity or otherphysical or physiological context. Accordingly, where it is desirable tocontinuously monitor a physiological signal, it can be advantageous toprocess the signal using the following techniques in order to reduce oreliminate the negative effects of confounding factors such as motion ofthe wearable, type of activity, the physical interface with a wearer'sskin, weather or other ambient conditions, and so forth.

FIG. 8 is a flow chart illustrating a method for concurrent use ofmultiple physiological parameter estimation techniques. For the sake ofclarity of explanation, the method 800 is described with respect toheart rate estimation techniques. It should be appreciated, however,that the method 800 can be extended to the concurrent use of parametersestimation techniques for any of various different physiologicalparameters. As described in greater detail below, each estimationtechnique may be optimized for different activities and conditions tofacilitate continuously and reliably estimating the physiologicalparameter.

The method 800 may be used in cooperation with any of the devices,systems, and methods described herein, such as by operating a wearablephysiological monitoring device to perform one or more of the followingsteps. The wearable physiological monitoring device may, for example, beany of the devices described herein, and may include a processor, amemory, and a physiological sensor such as a photoplesmythography sensoror other heart rate or physiological monitoring system to obtain aphysiological signal. The method 800 may also or instead be deployedusing a server such as a remote server coupled in communication with awearable physiological monitoring device and configured to receive datafrom the device and process the data using some or all of the stepsbelow.

As shown in step 802, the method 800 may include providing a pluralityof heart rate estimators for estimating a heart rate. Each one of theplurality of heart rate estimators may correspond to one of a number ofpredetermined measurement contexts for measuring the heart rate with awearable physiological monitor. For example, each one of the pluralityof heart rate estimators may be optimized to one of a number ofpredetermined measurement contexts so that the estimator is optimized toprovide an accurate calculation of the heart rate in the correspondingcontext. In general, the predetermined measurement contexts may relateto motion, ambient conditions, and combinations thereof. For example, itmay be useful to process data using one technique or algorithm whenoutdoors, e.g., in the presence of bright sunlight or darkness of night,while a different technique may be more useful while on a treadmill inan indoor facility with fixed, moderately bright lighting. Thus, thenumber of predetermined measurement contexts may include one or more ofan indoor activity, an outdoor activity. Similarly, weather conditionssuch as rain or temperature may affect the physical interface to a body,and conditions such as cloudiness or rain may impact measurements byaltering ambient lighting conditions. Thus, the number of predeterminedmeasurement contexts may also or instead include an ambient weathercondition.

In general, the heart rate signal estimated by the plurality of heartrate estimators may be a time domain heart rate signal such as aphotoplesmythography signal or other time domain measure of cardiacactivity. In another aspect, the estimators may be used to directlyestimate other derivative signals of interest such as heart ratevariability or the like. Thus the heart rate signal may be a heart ratevariability signal, peak-to-peak interval signal or other signal thatwould otherwise be calculated from the time domain heart rate.

Additionally, or alternatively, the number of predetermined measurementcontexts may include one or more of active, sedentary, and sleeping, anyof which may affect skin surface conditions, motion, and other factorsinfluencing the selection of an estimator for heart rate or any otherphysiological signal. The number of predetermined measurement contextsmay also or instead include one or more types of physical exercise. Forexample, different estimators may provide better results for activitiesthat vary significantly in terms of physical motion, strain, and soforth, particularly in activities such as bicycling, swimming, andjogging where the rate of movement and range of motion for thephysiological monitoring device is likely to vary widely. Similarly,other activities such as basketball or tennis may be less prone torecurring periodic motions, and may be amenable to treatment with other,different estimators of heart rate. Thus in another aspect, the numberof predetermined measurement contexts may include one or more types ofmotion of the wearable physiological monitor based on motion data fromone or more motion sensors in the wearable physiological monitor. Inanother aspect, different estimators may be more accurate for differentabsolute heart rates (e.g., high heart rates or low heart rates) or maybe more accurate for different relative heart rates (e.g., a percentageof the resting heart rate or maximum heart rate). Accordingly, incertain embodiments, the number of predetermined measurement contextsmay include a current heart rate estimated for the wearablephysiological monitor, which may, as noted above, include a relativeheart rate or an absolute heart rate.

The heart rate estimators may include a frequency domain peak detector.For example, the heart rate estimators may include a fundamentalfrequency of the physiological signal in the frequency domain or aharmonic product spectrum for the physiological signal. Additionally, oralternatively, the heart rate estimators may include a fundamentalfrequency of a complex cepstrum for the physiological signal. The heartrate estimators may also or instead include a peak detector for one ormore harmonics of the fundamental frequency.

In certain implementations, heart rate estimators may include at leastone estimator with a high-Q time domain filter for the physiologicalsignal around a predetermined frequency of interest. As an example, theheart rate may be estimated and the beats can be identified in the timedomain with this signal. The identification of the beats can be based ona likelihood analysis. A higher likelihood can be ascribed, for example,to higher rhythmicity of the signal. The absence of alternans(beat-to-beat variation in amplitude) may be additionally, oralternatively, associated with a higher likelihood. As anothernonexclusive example, a higher likelihood can be associated with notfinding a regular set of beats at ½ the estimated heart rate.

Further, or instead, the heart rate estimators may include an estimatorthat tracks peaks in the physiological signal relative to a motionsignal. For example, the actual peaks in the physiological signal can betracked over time and a motion signal frequency (e.g., based on a signalfrom a motion sensor) can be similarly tracked. Continuing with thisexample, when the actual peaks in the physiological signal and themotion signal frequency converge at the same estimated value, thelikelihood that this peak accurately represents the physiologicalparameter may be increased. Similarly, when the actual peaks in thephysiological signal and the motion signal frequency diverge, thelikelihood may decrease or return to normal.

The heart rate estimator may also or instead use any physiologicalsignal or data available to the monitoring device, or available in apost processing context, such as body temperature, breathing rate, andso forth. More generally, the heart rate estimators may be anyestimators that usefully facilitate a calculation of a heart rate underone or more conditions of interest. It will be appreciated that whilethe discussion herein emphasizes the use of heart rate signals inparticular, the techniques disclosed herein may also or instead be usedon any other physiological signal of interest. Thus, rather than a heartrate estimator, the estimators may include estimators of any time-basedphysiological signal of interest, with suitable adaptations to themonitoring system and the remaining method steps as would be appreciatedto one of ordinary skill in the art, and all such adaptations andmodifications are intended to fall within the scope of this disclosure.

As shown in step 804, the method 800 may include providing a pluralityof probability estimators. Each one of the plurality of probabilityestimators may correspond to one of the plurality of heart rateestimators, with each one of the plurality of probability estimatorsproviding a likelihood that the corresponding heart rate estimator isaccurately estimating the heart rate based on a physiological signalfrom the wearable physiological monitor.

The probability estimators may in general provide a manner forcalculating relative likelihoods that each of the plurality of heartrate estimators (or other physiological signal estimators) is producingan accurate or otherwise reliable result. This may be independentlycalculated for each estimator using any suitable scoring system or meritfunction, or this may be expressed as relative values where the sum ofthe likelihoods for all of the estimators is 1.0 or 100 percent, or someother normalized value. In one aspect, the probability estimators mayuse time-based information, e.g., where the most recently selectedestimator, or one of the last several recently selected estimators(e.g., last two, three, five, or some other number) receives anincreased score based on an inference that the estimator remainsaccurate or relevant. Thus, for example, where an indoor estimator hasbeen selected, that estimator may continue to be applied until a strongcontrary inference is presented for an outdoor estimator.

The probability estimators may also be varied or weighted in differentways. For example, the probability estimators may be hierarchicallyarranged so that groups of estimators are scored in groups independentlyfrom one another, relative to one another, or some combination of these.In another aspect, probability estimators may be weighted or adjustedbased on other data external to the current physiological monitoringsuch as GPS data, user history data, weather information, and so forth.

In one aspect, suitably likelihood functions may be analytically derivedfrom known characteristics of the physiological system, physicalactivities, monitoring device, and so forth. In another aspect, suitablelikelihood functions for use as probability estimators may be derivedusing representative data sets and ensemble classifiers, as describedfor example in U.S. Prov. App. No. 62/218,017 filed on Sep. 14, 2015 andincorporated by reference herein in its entirety. More generally, anytechniques useful for discriminating among multiple options forcalculating a heart rate or other time-based metric of interest fromphysiological data may be usefully employed as a probability estimatoras contemplated herein.

As shown in step 806, the method 800 may include acquiring aphysiological signal from the wearable physiological monitor over aninterval. The interval may be any time interval of interest subject tothe data recording and storage limits of the monitoring system(including any remote processing and storage resources), and may be madeup of segments (e.g., discrete segments), which may, for example, bemutually exclusive segments that together cover the entire interval or apredetermined portion thereof. In another aspect, the segments may beoverlapping segments or any other windowed or otherwise selected andarranged segments useful for applying probability estimators ascontemplated herein. The physiological signal may, for example, includea photoplesmythography signal as described above, or any otherphysiological signal indicative of a physiological state that might varyover time such as a breathing rate, body temperature, pulse oxygenlevel, and so forth.

As shown in step 808, the method 800 may include, for each segment ofthe interval, assigning one or more selected ones of the plurality ofheart rate estimators to the segment according to the likelihood ofaccurately estimating the heart rate in the segment. In one aspect, theheart rate estimator with the highest score or likelihood may beselected to calculate the heart rate for that interval. In anotheraspect, additional rules or the like may be applied to select amongvarious candidates. Thus for example, assigning one of the heart rateestimators to a segment may include favorably weighting one or moreimmediately prior estimators prior to selecting one of the heart rateestimators for assignment to the segment. In a post-processing context,this may also or instead include favorably weighting one or moresubsequent heart rate estimators. Further, whether or not thesetemporally adjacent heart rate estimators are weighted more favorablymay depend on a relative score or confidence associated with them, sothat a very highly scored and immediately adjacent estimator is morelikely to be applied to a current segment. In another aspect, estimatorsmay be hierarchically arranged to control the assignment process. Forexample, where one heart rate estimator is selected, other heart rateestimators may be more favorably weighted or less favorably weightedrelative to other estimators according to the nature of the relationshipamong estimators within the group—e.g., based on whether selection ofone estimator in the group makes other estimators in the group more orless likely to occur in temporally adjacent segments.

In another aspect, a threshold or the like may be applied to discerncircumstances in which none of a set of heart rate estimators appears tobe providing reliable data. In this case, any number of techniques maybe used to interpolate heart rate data until a reliable signal isrestored. For example, this may include using an immediately priorestimator, using a simple fundament frequency, using an average of thephysiological signal over some preceding interval, or any other suitabletechnique. Alternatively, the error condition may be reported as missingdata and processing may be suspended or terminated until a reliablephysiological signal is reacquired.

It will be understood that a variety of alternative implementations ofthis general approach may usefully be employed. For example, apreliminary estimate may be created of a most probable heart rate basedon one or more factors such as a prior heart rate, a subsequent heartrate, or a history for a user of the wearable physiological monitor, aswell as combinations of the foregoing and/or any other suitableinformation or inputs. This preliminary estimate may also or instead befiltered to remove motion artifacts as described herein. In any case,after creating a preliminary estimate, this preliminary estimate may beadjusted by applying the plurality of heart rate estimators according tothe probability estimators to increase a probability of an accurateestimate as generally contemplated herein.

In another aspect, multiple heart rate estimators may be combined forconcurrent use in order to increase accuracy. Similar to a traditionalmultiple imputations approach, the fusion step in such aselection—fusion algorithm may combine the results of the individualclassifiers to boost-up the overall classification accuracy. In multipleimputations, the results are combined in a simple fashion:

${\Gamma_{MI}(x)} = {\frac{1}{q}{\sum\limits_{i = 1}^{q}\;{\Gamma\left( {x;I_{i}} \right)}}}$

where I_(i) is the ith imputation of the incomplete data and qrepresents the number of imputations. The number of required imputationsis estimated by the Rubin's imputation efficiency law quantified by

${efficiency} = \frac{1}{\left( {1 + \frac{\gamma}{q}} \right)^{0.5}}$

where γ is the fraction of the missing values in the data. Theefficiency is a value between 0 and 1 and shows the performance of qimputations compared with the infinite number of imputations. When q issmall compared with γ, increasing q improves the efficiency. However,when q is large enough, its further increments do not improve theefficiency considerably. This criterion may be used to selectappropriate number of imputations.

In the contemplated selection-fusion method, the distribution of themissing values in the feature space may be used to improve theperformance. In contrast to a multiple imputation approach where allimputations have the same weight, in the proposed approach, theclassification accuracy of each classifier for a given testing samplecan be used to weigh the outputs. Since a subset of samples andfeatures, not the whole data, is involved in the training of eachclassifier, a specific subset may be advantageous depending on thesample being tested. Thus, in the fusion step, the aggregation step maybe the weighted combination:

${\Gamma_{BB}(x)} = {\frac{1}{\sum\limits_{i}\text{1/φ}_{i,x}}{\sum\limits_{i = 1}^{n{(B)}}\;{\frac{1}{\varphi_{i,x}}{\Gamma\left( {x;S_{\theta_{i}}} \right)}}}}$

where φ_(i,x) is the relative inaccuracy or expected error ofΓ(x;S_(θi)) estimated at x which depends on the accuracy of Γ(x;S_(θi))around x and the number of features used in the classifier.

By way of non-limiting example, two factors may be usefully consideredin determining a classifier's expected error φ_(i,x) for a specificsample: (1) general accuracy of the classifier and (2) similaritybetween the features of the samples in the training set and those of thetesting sample. Thus, the local accuracy of the classifier should becalculated for each individual testing sample based on two factors: (1)the number of samples in the training set that are in the neighborhoodof the testing sample and (2) the similarity between the subset features(θ_(i)) and the existing features for the testing sample.

Next a similarity may be estimated between the training and testingsamples. If all features are identically informative, the similaritybetween the missing value patterns in a subset and the testing samplecan be characterized by {circumflex over (θ)}_(x) _(i) ^(T)θ_(i) where{circumflex over (θ)}_(x) _(i) and θ_(i) are the feature sets availablefor the testing sample x_(j) and the ith subset, respectively. To takethe relative quality of the features into account, the similarity iswritten as θ_(x) _(i) ^(T)Kθ_(i) where K is a diagonal matrix to weighthe features based on their information level.

A value for φ_(i,x) may be calculated usingφ_(i,x)=(Γ(x;S _(θ) _(i) )−Y(x))²ƒ(θ_(x) _(i) ^(T) Kθ _(i))where Y(x) is the label of x. When there is no ranking of the features,K is equal to the identity matrix. Here, ƒ is a non-increasing functionthat calculates the effect of similarity between the feature spaces ofthe classifier and the testing sample. For simplicity, we define ƒ(u) as1/u. When there are no common features, ƒ removes the effect of theclassifier from aggregation. Alternatively, when all features arepresent, ƒ does not change the error measure.

The preceding equation can be calculated for the training data. However,for a testing sample, it needs to be estimated since Y(x) is unknown. Toestimate φ_(i,x) easily, all of the training samples in the vicinity ofthe testing sample may be used:

${\hat{\varphi}}_{i,x^{\prime}} = {\frac{1}{\eta_{x^{\prime}}}{\sum\limits_{x \in {Training}}\;{{{dis}\left( {x,x^{\prime}} \right)}\varphi_{i,x}}}}$where(dis(x,x′))² =∥x−x′∥ ²ƒ(θ_(x) ^(T) Kθ _(x′))andη_(x′)=Σ_(xεTraining)dis(x,x′)

It will be noted that the distance between the two samples is modulatedby their common features through the second term. Using this fusiontechnique, a number of heartbeat estimators may be used in combinationto improve accuracy of an estimated physiological signal such as a heartrate. Thus in one aspect, assigning one or more selected ones of theplurality of heart rate estimators to a segment of a measurementinterval may include fusing multiple heart rate estimators for thesegment using the techniques described above and using this fusedestimator to determine a heart rate for the segment, e.g., by estimatingthe heart rate signal with a fused estimator or applying the fusedfunction to adjust a heart rate determined using another estimator ortechnique.

As shown in step 810, the method 800 may include providing a heart ratesignal over the interval based upon the physiological signal and theselected ones of the plurality of heart rate estimators. In general, theheart rate estimator assigned to each segment may be used to calculatethe heart rate for that segment. With a heart rate signal calculatedfrom raw source data (e.g., the sensor data captured directly by themonitoring device) for each segment using an accurate estimator in thismanner, the heart rate signal for the entire interval can be determinedas a combination of the heart rate signal for each of the individualsegments.

It should be appreciated that, because the heart rate signal provided instep 810 is continuous, the method 800 may further include using theheart rate signal for assessment of health, fitness, recovery, and/orsleep according to any one or more of the various different methodsdescribed herein that rely upon or benefit from a continuousphysiological signal. For example, the method 800 may further includedetermining heart rate variability over the interval based upon theheart rate signal. The heart rate variability can be determinedaccording to any or more of the various different methods describedherein, such as the methods described above with respect to FIG. 3.

This approach provides significant advantages over other techniquespresent in the art. The use of multiple estimators and correspondinglikelihood functions presents additional processing and storagerequirements generally unsuited to a wearable device. Further therewould appear to be little motivation to adapt existing wearable monitorsfor this use where the primary purpose is display of a current heartrate or acquisition of periodic heart rate measurements over time.However, where a metric such as workout intensity is being calculated,the result is highly dependent on heart rate variability throughout theexercise activity, and missing data may lead to substantial errors inthe appraisal of a workout's difficulty, or similarly, the recoveryobtained from a period of rest or sleep. By facilitating the derivationof a more accurate continuous measurement of heart rate variability,downstream calculations of metrics that are highly sensitive to accurateHRV measurements—such as exercise intensity and recovery—can besubstantially improved.

According to the foregoing, a system contemplated herein includes amemory and a server. The memory may generally be configured to storedata corresponding to a physiological signal over an interval, thephysiological signal acquired by a wearable physiological monitor. Theserver may be configured to assign, for each segment of the interval,one or more selected heart rate estimators of a plurality of heart rateestimators based on a likelihood of accurately estimating the heart ratein the segment and to provide a continuous heart rate signal over theinterval based upon the physiological signal and the one or moreselected heart rate estimators, each heart rate estimator of theplurality of heart rate estimators optimized for one of a number ofpredetermined measurement contexts for measuring the heart rate with thewearable physiological monitor.

In the system, the likelihood of accurately estimating the heart rate inthe segment may be based on a plurality of probability estimators, eachone of the plurality of probability estimators corresponding to one ofthe plurality of heart rate estimators, and each one of the plurality ofprobability estimators providing a likelihood that the correspondingheart rate estimator is accurately estimating the heart rate based onthe physiological signal from the wearable physiological monitor. Inanother aspect, the plurality of heart rate estimators may include atleast one or more of a frequency domain peak detector, a fundamentalfrequency of a harmonic product spectrum for the physiological signal, afundamental frequency of a complex cepstrum for the physiologicalsignal, a high-Q time domain filter for the physiological signal arounda predetermined frequency of interest, and an estimator that trackspeaks in the physiological signal relative to a motion signal.

FIG. 9 is a flow chart illustrating a signal processing algorithm forremoving motion artifacts from a physiological signal. Removing motionartifacts from the physiological signal according to method 900 canreduce the likelihood that movement of a wearer of a wearable continuousphysiological monitoring device will interfere with continuous andreliable physiological monitoring. That is, in general, the method 900can facilitate physiological monitoring in a manner that is robust withrespect to motion typically associated with exercise.

The method 900 may be used in cooperation with any of the devices,systems, and methods described herein, such as by operating a wearablephysiological monitoring device to perform one or more of the followingsteps. The wearable physiological monitoring device may, for example,include a processor, a memory, and a physiological sensor (e.g., one ormore light emitting diodes and one or more light detectors arranged toobtain heart rate data from a wearer of the wearable device) to obtain aphysiological signal. Additionally, or alternatively, the method 900 maybe implemented on a server (e.g., a remote server) in communication witha wearable physiological monitoring device to perform one or more of thefollowing steps. As an example, the server can receive a physiologicalsignal obtained by the wearable physiological monitoring device and oneor more steps of method 900 may be performed at the server.

As shown in step 901, the method 900 may include modeling a system thatincludes a wearable physiological device and a user. In general, themodeling may seek to determine a relationship between motion by thewearable device and physiological measurements taken by the device,e.g., optical signals from a photoplesmythography system, and may resultin a model suitable for reducing or eliminating motion artifacts from aphysiological measurement. The relationship may be determined using anysuitable modeling techniques including, without limitation, regressionanalysis, physical modeling, empirical modeling, matched filtering,machine learning, or any other suitable time domain, frequency domain,probabilistic, or other techniques. In one aspect, a weighting functionmay be derived with minimum weights at dominant peaks of anaccelerometer spectrum, e.g., for each of three accelerometer signals ina three-dimensional motion sensing system. The dominant peaks may alsoor instead be estimated and separately applied to form a weightingfunction for motion cancellation. In another aspect, various harmonicsmay also or instead be used to form a weighting function to account formodulation of three-dimensional motion within the physical system. Inanother aspect, motion projection can be employed to derive a model forprojecting the motion signal into a common space with the optical signal(or other sensor signal) for direct mathematical combination. A modelthat relates the motion of the device to a physiological measurement mayalso or instead be based on a multiplicative modulation of thephysiological signal and the motion signal. With this model, a set ofharmonics of the underlying physiological signal and the motion will beobserved. The model, in certain instances, may be a function of themotion normal to the tissue.

Suitable techniques for developing a model for a relationship betweenphysical motion of a wearable monitor and a physiological signalobtained from a sensor of the wearable monitor are described by way ofnon-limiting examples in U.S. Prov. App. No. 62/218,017 filed on Sep.14, 2015 and incorporated by reference herein in its entirety. These anyother techniques may usefully be employed to derive a model suitable foruse in the method 900 contemplated herein.

As shown in step 902, the method 900 may include acquiring aphysiological signal with the device over time. The physiological signalmay be indicative of a physical state of a wearer of the device. In oneaspect, the physiological signal may be a signal indicative of a heartrate of the wearer. By way of example, and not limitation, such aphysiological signal may be acquired with a photoplesmythography (PPG)detector in the device. The PPG detector, it should be appreciated, maybe any of the various different PPG detectors described herein. Thephysiological signal may also or instead include any optical,electrical, acoustic, or other signal indicative of a physical state ofthe user, such as a heart rate, heart rate variability, bodytemperature, breathing rate, breathing volume, pulse oxygen, bloodpressure, and so forth.

As shown in step 904, the method 900 may include measuring motion of thedevice as a motion signal over time. As an example, the device mayinclude one or more accelerometers, and the measured motion of thedevice may be based on a motion signal from the one or moreaccelerometers over time. It should be appreciated that the one or moreaccelerometers may be any of the various, different accelerometersgenerally known in the art and, therefore, may include multi-axisaccelerometers. For example, the device may include a three-axis motionsensing system that includes three accelerometers to capture motion datain three-dimensions, e.g. along three orthogonal axes. In addition, orin the alternative, measuring motion of the device may include detectingmotion in a predetermined axis (e.g., an x-, y-, or z-axis) of thedevice, which may be aligned with a single accelerometer axis or derivedfrom a multi-axis sensing system.

As shown in step 906, the method 900 may include determining afundamental frequency of a spectrum of the motion signal. This mayinclude determining the fundamental frequency using any suitabletechnique or combination of techniques. For example, a dominant peak inthe spectrum of the motion signal may be identified. Further, orinstead, determining the fundamental frequency may include identifyingthe fundamental frequency using at least one of a harmonic productspectrum and a complex cepstrum of the spectrum of the motion signal. Avariety of useful models as contemplated in step 901 may convenientlycharacterize motion artifacts as a set of harmonics of a fundamentalmotion frequency. Thus a model to address these artifacts may usefullymodify a sensor signal using a group of notch filters that selectivelyattenuate the physiological signal (in a weighted or unweighted manner,or some combination of these) at the fundamental frequency of the motionsignal and any number of harmonics thereof. As a significant advantage,this approach facilitates artifact mitigation using a measurement of asingle fundamental frequency of the motion signal (or alternatively,three fundamental frequencies along three orthogonal axes). In certainimplementations, the spectrum of the motion signal may be preprocessedto remove one or more artifacts arising from time domain discontinuitiesin the motion signal. An exemplary preprocessing step may includediscarding any peaks caused by a 1/f frequency distribution, such asmight be caused by discontinuities in the motion signal.

As shown in step 908, the method 900 may include applying a model to thephysiological signal to mitigate motion artifacts. In one aspect, thismay include a notch filter to the physiological signal, such as a notchfilter or group of notch filters that attenuate a fundamental frequencyof the motion signal and one or more related frequencies of the motionsignal such as harmonics of the fundamental frequency. The filter orgroup of filters may thus provide a filtered physiological signal withreduced motion artifacts for subsequent processing.

Applying the notch filter may include, for example, post-processing thephysiological signal on a remote computing resource that receives thephysiological signal and the motion signal from the device. The remotecomputing resource may be any of the various, different remote computingresources described herein. It should be appreciated thatpost-processing the physiological signal on the remote computingresource may advantageously facilitate reducing the size of the wearabledevice, given that post-processing the physiological signal on theremote computing resource can reduce the memory and/or processingrequirements associated with the wearable device, and may furtherprovide access to computational resources that cannot reasonably bedeployed within a wearable continuous physiological monitoring device.

In one aspect, a system contemplated herein includes a memory configuredto store a physiological signal over time, the physiological signalindicative of a physical state of a wearer of a device for physiologicalmonitoring, and the memory further configured to store a motion signalof the device over time; and a server configured to filter thephysiological signal based on applying a notch filter to thephysiological signal, wherein the notch filter attenuates a fundamentalfrequency of the motion signal and one or more related frequencies ofthe motion signal. The one or more related frequencies may include atleast one of the following: one or more harmonics of the fundamentalfrequency and a number of frequencies selected based on a physical modelof the device and the wearer, the physical model includingmultiplicative modulation of the physiological signal and the motionsignal.

In another aspect, a method contemplated herein may include developingand applying a model for artifact mitigation. Thus, there is describedherein a method for mitigating motion artifacts in a physiologicalsignal from a wearable physiological monitoring device. The method mayinclude acquiring a physiological signal with a device over time,wherein the device is a wearable device for physiological monitoringincluding a physiological sensor to capture the physiological signal,and further wherein the physiological signal is indicative of a physicalstate of a wearer of the device; measuring motion of the device as amotion signal over time, wherein the motion signal includesthree-dimensional motion data from a three-axis motion sensing system ofthe device; determining a fundamental frequency of a spectrum of themotion signal in at least one axis; providing a model establishing arelationship between three-dimensional motion of the device and thephysiological signal obtained from the physiological sensor; applyingthe model to remove a motion artifact in the physiological signal causedby the three-dimensional motion of the device based on thethree-dimensional motion data from the three-axis motion sensing system.

The above systems, devices, methods, processes, and the like may berealized in hardware, software, or any combination of these suitable forthe control, data acquisition, and data processing described herein.This includes realization in one or more microprocessors,microcontrollers, embedded microcontrollers, programmable digital signalprocessors or other programmable devices or processing circuitry, alongwith internal and/or external memory. This may also, or instead, includeone or more application specific integrated circuits, programmable gatearrays, programmable array logic components, or any other device ordevices that may be configured to process electronic signals. It willfurther be appreciated that a realization of the processes or devicesdescribed above may include computer-executable code created using astructured programming language such as C, an object orientedprogramming language such as C++, or any other high-level or low-levelprogramming language (including assembly languages, hardware descriptionlanguages, and database programming languages and technologies) that maybe stored, compiled or interpreted to run on one of the above devices,as well as heterogeneous combinations of processors, processorarchitectures, or combinations of different hardware and software.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. The code may be stored in a non-transitoryfashion in a computer memory, which may be a memory from which theprogram executes (such as random access memory associated with aprocessor), or a storage device such as a disk drive, flash memory orany other optical, electromagnetic, magnetic, infrared or other deviceor combination of devices. In another aspect, any of the systems andmethods described above may be embodied in any suitable transmission orpropagation medium carrying computer-executable code and/or any inputsor outputs from same. In another aspect, means for performing the stepsassociated with the processes described above may include any of thehardware and/or software described above. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

It should further be appreciated that the methods above are provided byway of example. Absent an explicit indication to the contrary, thedisclosed steps may be modified, supplemented, omitted, and/orre-ordered without departing from the scope of this disclosure.

It will be appreciated that the methods and systems described above areset forth by way of example and not of limitation. Numerous variations,additions, omissions, and other modifications will be apparent to one ofordinary skill in the art. In addition, the order or presentation ofmethod steps in the description and drawings above is not intended torequire this order of performing the recited steps unless a particularorder is expressly required or otherwise clear from the context.

The method steps of the implementations described herein are intended toinclude any suitable method of causing such method steps to beperformed, consistent with the patentability of the following claims,unless a different meaning is expressly provided or otherwise clear fromthe context. So for example performing the step of X includes anysuitable method for causing another party such as a remote user, aremote processing resource (e.g., a server or cloud computer) or amachine to perform the step of X. Similarly, performing steps X, Y and Zmay include any method of directing or controlling any combination ofsuch other individuals or resources to perform steps X, Y and Z toobtain the benefit of such steps. Thus method steps of theimplementations described herein are intended to include any suitablemethod of causing one or more other parties or entities to perform thesteps, consistent with the patentability of the following claims, unlessa different meaning is expressly provided or otherwise clear from thecontext. Such parties or entities need not be under the direction orcontrol of any other party or entity, and need not be located within aparticular jurisdiction.

It will be appreciated that the methods and systems described above areset forth by way of example and not of limitation. Numerous variations,additions, omissions, and other modifications will be apparent to one ofordinary skill in the art. In addition, the order or presentation ofmethod steps in the description and drawings above is not intended torequire this order of performing the recited steps unless a particularorder is expressly required or otherwise clear from the context. Thus,while particular embodiments have been shown and described, it will beapparent to those skilled in the art that various changes andmodifications in form and details may be made therein without departingfrom the spirit and scope of this disclosure and are intended to form apart of the invention as defined by the following claims, which are tobe interpreted in the broadest sense allowable by law.

What is claimed is:
 1. A method for concurrent use of multiple heartrate estimation techniques, the method comprising: providing a pluralityof heart rate estimators for estimating a heart rate, each one of theplurality of heart rate estimators optimized for one of a number ofpredetermined measurement contexts for measuring the heart rate with awearable physiological monitor; providing a plurality of probabilityestimators, each one of the plurality of probability estimatorscorresponding to one of the plurality of heart rate estimators, and eachone of the plurality of probability estimators providing a likelihoodthat a corresponding heart rate estimator is accurately estimating theheart rate based on a physiological signal from the wearablephysiological monitor; acquiring a physiological signal from thewearable physiological monitor over an interval having segments; foreach segment of the interval, assigning one or more selected ones of theplurality of heart rate estimators to the segment according to thelikelihood of accurately estimating the heart rate in the segment; andproviding a heart rate signal over the interval based upon thephysiological signal and the selected ones of the plurality of heartrate estimators.
 2. The method of claim 1, wherein assigning one or moreselected ones of the plurality of heart rate estimators includes fusingmultiple heart rate estimators to create a fused estimator and applyingthe fused estimator to determine a heart rate for the segment.
 3. Themethod of claim 1, further comprising determining a heart ratevariability over the interval based upon the heart rate signal.
 4. Themethod of claim 1, wherein the heart rate signal is a heart ratevariability signal.
 5. The method of claim 1, wherein the number ofpredetermined measurement contexts include one or more of an indooractivity, an outdoor activity, one or more physiological parameters, andan ambient weather condition.
 6. The method of claim 1, wherein thenumber of predetermined measurement contexts include one or more ofactive, sedentary, and sleeping.
 7. The method of claim 1, wherein thenumber of predetermined measurement contexts include one or more typesof physical exercise.
 8. The method of claim 1, wherein the number ofpredetermined measurement contexts include one or more types of motionof the wearable physiological monitor based on motion data from one ormore motion sensors in the wearable physiological monitor.
 9. The methodof claim 1, wherein the number of predetermined measurement contextsinclude a current heart rate estimated for the wearable physiologicalmonitor.
 10. The method of claim 1, wherein the heart rate estimatorsinclude a frequency domain peak detector for detecting at least one of afundamental peak and one or more harmonic peaks.
 11. The method of claim1, wherein the heart rate estimators include a fundamental frequency ofa harmonic product spectrum for the physiological signal.
 12. The methodof claim 1, further comprising creating a preliminary estimate of a mostprobable heart rate based on one or more factors, and after creating thepreliminary estimate, applying the plurality of heart rate estimatorsbased on the probability estimators to increase a probability of anaccurate estimate.
 13. The method of claim 12, wherein the preliminaryestimate is based on one or more of a prior heart rate, a subsequentheart rate, and a history for a user of the wearable physiologicalmonitor.
 14. The method of claim 12, wherein the preliminary estimate isfiltered to remove one or more motion artifacts before applying theplurality of probability estimators.
 15. The method of claim 1, whereinthe heart rate estimators include at least one estimator with a high-Qtime domain filter for the physiological signal around a predeterminedfrequency of interest.
 16. The method of claim 1, wherein the heart rateestimators include an estimator that tracks peaks in the physiologicalsignal relative to a motion signal.
 17. A computer program productcomprising non-transitory computer executable code embodied in anon-transitory computer-readable medium that, when executing on one ormore computing devices, performs the steps of: providing a plurality ofheart rate estimators for estimating a heart rate, each one of theplurality of heart rate estimators corresponding to one of a number ofpredetermined measurement contexts for measuring the heart rate with awearable physiological monitor; providing a plurality of probabilityestimators, each one of the plurality of probability estimatorscorresponding to one of the plurality of heart rate estimators, and eachone of the plurality of probability estimators providing a likelihoodthat a corresponding heart rate estimator is accurately estimating theheart rate based on a physiological signal from the wearablephysiological monitor; acquiring a physiological signal from thewearable physiological monitor over an interval having segments; foreach segment, assigning one or more selected ones of the plurality ofheart rate estimators to the segment based upon the likelihood ofaccurately estimating the heart rate over in the segment; and providinga heart rate signal over the interval based upon the physiologicalsignal and the selected ones of the plurality of heart rate estimators.18. The computer program product of claim 17, wherein the segments arediscrete segments of the interval.
 19. The computer program product ofclaim 17, wherein the heart rate signal is continuously provided overthe interval.
 20. The computer program product of claim 17, wherein thenumber of predetermined measurement contexts include one or more typesof physical activity.
 21. The computer program product of claim 17,wherein the plurality of heart rate estimators include at least one ormore of a frequency domain peak detector, a fundamental frequency of aharmonic product spectrum for the physiological signal, a fundamentalfrequency of a complex cepstrum for the physiological signal, a high-Qtime domain filter for the physiological signal around a predeterminedfrequency of interest, and an estimator that tracks peaks in thephysiological signal relative to a motion signal.
 22. A systemcomprising: a memory configured to store data corresponding to aphysiological signal over an interval, the physiological signal acquiredby a wearable physiological monitor; and a server configured to assign,for each segment of the interval, one or more selected heart rateestimators of a plurality of heart rate estimators based on a likelihoodof accurately estimating the heart rate in the segment and to provide acontinuous heart rate signal over the interval based upon thephysiological signal and the one or more selected heart rate estimators,each heart rate estimator of the plurality of heart rate estimatorsoptimized for one of a number of predetermined measurement contexts formeasuring the heart rate with the wearable physiological monitor. 23.The system of claim 22, wherein the likelihood of accurately estimatingthe heart rate in the segment is based on a plurality of probabilityestimators, each one of the plurality of probability estimatorscorresponding to one of the plurality of heart rate estimators, and eachone of the plurality of probability estimators providing a likelihoodthat a corresponding heart rate estimator is accurately estimating theheart rate based on the physiological signal from the wearablephysiological monitor.
 24. The system of claim 22, wherein the pluralityof heart rate estimators include at least one or more of a frequencydomain peak detector, a fundamental frequency of a harmonic productspectrum for the physiological signal, a fundamental frequency of acomplex cepstrum for the physiological signal, a high-Q time domainfilter for the physiological signal around a predetermined frequency ofinterest, and an estimator that tracks peaks in the physiological signalrelative to a motion signal.